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corl_2023_VLihM67Wdi6 | VLihM67Wdi6 | corl | 2,023 | STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience | Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation. Current approaches that provide robots with this awareness either rely on labeled data which is expensive to collect, engineered features... | Haresh Karnan;Elvin Yang;Daniel Farkash;Garrett Warnell;Joydeep Biswas;Peter Stone | University of Texas, Austin;University of Texas at Austin;;Army Research Laboratory;The University of Texas at Austin;University of Texas, Austin | Poster | main | Vision-based Navigation;Representation Learning;Learning from Experience | https://github.com/HareshKarnan/sterling_corl23 | https://openreview.net/forum?id=VLihM67Wdi6 | 23 | STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation. Current approaches that provide robots with... | [
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corl_2023_VscdYkKgwdH | VscdYkKgwdH | corl | 2,023 | Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control | We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidan... | Songyuan Zhang;Kunal Garg;Chuchu Fan | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Distributed control;Control barrier functions;Graph neural networks | https://openreview.net/forum?id=VscdYkKgwdH | 33 | Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using o... | [
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corl_2023_VtJqMs9ig20 | VtJqMs9ig20 | corl | 2,023 | CAT: Closed-loop Adversarial Training for Safe End-to-End Driving | Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a \textbf{C}losed-loop \textbf{A}dversarial \textbf{T}raining (CAT) framework for safe end-to-end driving in this paper through the lens of e... | Linrui Zhang;Zhenghao Peng;Quanyi Li;Bolei Zhou | Tsinghua University;University of California, Los Angeles;Shanghai Artificial Intelligence Laboratory;University of California, Los Angeles | Poster | main | Safety-Critical Scenario Generation;Adversarial Training;End-to-End Driving | https://github.com/metadriverse/cat | https://openreview.net/forum?id=VtJqMs9ig20 | 33 | CAT: Closed-loop Adversarial Training for Safe End-to-End Driving
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a \textbf{C}losed-loop \textbf{A}dversarial \textbf{T}raining (CAT) framewo... | [
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corl_2023_VtUZ4VGPns | VtUZ4VGPns | corl | 2,023 | IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors | Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distribution shift by allowing robots to access remote human supervisors during task exe... | Gaurav Datta;Ryan Hoque;Anrui Gu;Eugen Solowjow;Ken Goldberg | University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;;University of California, Berkeley | Poster | main | Imitation Learning;Fleet Learning;Energy-Based Models | https://github.com/BerkeleyAutomation/IIFL | https://openreview.net/forum?id=VtUZ4VGPns | 5 | IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distribut... | [
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corl_2023_W0zgY2mBTA8 | W0zgY2mBTA8 | corl | 2,023 | ChainedDiffuser: Unifying Trajectory Diffusion and Keypose Prediction for Robotic Manipulation | We present ChainedDiffuser, a policy architecture that unifies action keypose prediction and trajectory diffusion generation for learning robot manipulation from demonstrations. Our main innovation is to use a global transformer-based action predictor to predict actions at keyframes, a task that requires multi- modal s... | Zhou Xian;Nikolaos Gkanatsios;Theophile Gervet;Tsung-Wei Ke;Katerina Fragkiadaki | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Manipulation;Imitation Learning;Transformers;Diffusion Models | https://github.com/zhouxian/chained-diffuser | https://openreview.net/forum?id=W0zgY2mBTA8 | 88 | ChainedDiffuser: Unifying Trajectory Diffusion and Keypose Prediction for Robotic Manipulation
We present ChainedDiffuser, a policy architecture that unifies action keypose prediction and trajectory diffusion generation for learning robot manipulation from demonstrations. Our main innovation is to use a global transfor... | [
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corl_2023_W5SrUCN0yUa | W5SrUCN0yUa | corl | 2,023 | A Bayesian Approach to Robust Inverse Reinforcement Learning | We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior... | Ran Wei;Siliang Zeng;Chenliang Li;Alfredo Garcia;Anthony D McDonald;Mingyi Hong | Texas A&M University - College Station;University of Minnesota, Twin Cities;The Chinese University of Hong Kong;Texas A&M University - College Station;University of Wisconsin - Madison;University of Minnesota, Minneapolis | Poster | main | Inverse Reinforcement Learning;Bayesian Inference;Robustness | https://github.com/rw422scarlet/bmirl_tf | https://openreview.net/forum?id=W5SrUCN0yUa | 7 | A Bayesian Approach to Robust Inverse Reinforcement Learning
We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective m... | [
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corl_2023_W7eg2NqFJ60 | W7eg2NqFJ60 | corl | 2,023 | Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism | This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the limited availability of guide animals that are accessible to only two to three percent of the potential blind or visually impaired (BVI) us... | J. Taery Kim;Wenhao Yu;Yash Kothari;Bruce Walker;Jie Tan;Greg Turk;Sehoon Ha | Google;Georgia Institute of Technology;Georgia Institute of Technology;Google;;Georgia Institute of Technology;Georgia Institute of Technology | Poster | main | Assistive Robot;Autonomous Navigation;Interaction Modeling | https://openreview.net/forum?id=W7eg2NqFJ60 | 13 | Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism
This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the l... | [
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corl_2023_W8MjsxHrDpL | W8MjsxHrDpL | corl | 2,023 | Synthesizing Navigation Abstractions for Planning with Portable Manipulation Skills | We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large amounts of data and fail to generalize learned abstractions to new environments. To address this, we propose to exploit the independence between spatial and non-spatial state variabl... | Eric Rosen;Steven James;Sergio Orozco;Vedant Gupta;Max Merlin;Stefanie Tellex;George Konidaris | Brown University;University of the Witwatersrand;Brown University;Brown University;Brown University;, Brown University;Brown University | Poster | main | Learning Abstractions;Mobile Manipulation | https://github.com/ericrosenbrown/aosm_experiments | https://openreview.net/forum?id=W8MjsxHrDpL | 5 | Synthesizing Navigation Abstractions for Planning with Portable Manipulation Skills
We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large amounts of data and fail to generalize learned abstractions to new environments. To address this, ... | [
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corl_2023_WGSR7HDuHu | WGSR7HDuHu | corl | 2,023 | Learning Robot Manipulation from Cross-Morphology Demonstration | Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the casewhere the teacher’s morphology is substantially different from that of the student. Our framework, Morphological Adaptation in Imitation Learning (MAIL), bridges this gap allo... | Gautam Salhotra;I-Chun Arthur Liu;Gaurav S. Sukhatme | ;University of Southern California;University of Southern California | Poster | main | Imitation from Observation;Learning from Demonstration | https://github.com/uscresl/mail | https://openreview.net/forum?id=WGSR7HDuHu | 8 | Learning Robot Manipulation from Cross-Morphology Demonstration
Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the casewhere the teacher’s morphology is substantially different from that of the student. Our framework, Morphologica... | [
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corl_2023_WWiKBdcpNd | WWiKBdcpNd | corl | 2,023 | HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation | Tracing – estimating the spatial state of – long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (over 25) crossings, w... | Vainavi Viswanath;Kaushik Shivakumar;Mallika Parulekar;Jainil Ajmera;Justin Kerr;Jeffrey Ichnowski;Richard Cheng;Thomas Kollar;Ken Goldberg | University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Carnegie Mellon University;Toyota Research Institute;Toyota Research Institute;University of California, Berkeley | Oral | main | state estimation;deformable manipulation | https://github.com/vainaviv/handloom | https://openreview.net/forum?id=WWiKBdcpNd | 6 | HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation
Tracing – estimating the spatial state of – long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For lo... | [
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corl_2023_WmF-fagWdD | WmF-fagWdD | corl | 2,023 | SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation | We propose SCALE, an approach for discovering and learning a diverse set of interpretable robot skills from a limited dataset. Rather than learning a single skill which may fail to capture all the modes in the data, we first identify the different modes via causal reasoning and learn a separate skill for each of them. ... | Tabitha Edith Lee;Shivam Vats;Siddharth Girdhar;Oliver Kroemer | Carnegie Mellon University;;Carnegie Mellon University;Carnegie Mellon University | Poster | main | skill discovery;causal learning;manipulation | https://openreview.net/forum?id=WmF-fagWdD | 9 | SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation
We propose SCALE, an approach for discovering and learning a diverse set of interpretable robot skills from a limited dataset. Rather than learning a single skill which may fail to capture all the modes in the data, we first identify the... | [
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corl_2023_WuBv9-IGDUA | WuBv9-IGDUA | corl | 2,023 | Multi-Resolution Sensing for Real-Time Control with Vision-Language Models | Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks. Multi-spatial resolution sensing provides hierarchical information captured at different spatial scales and enables both coarse and precise motions. Simultaneously multi-temporal resoluti... | Saumya Saxena;Mohit Sharma;Oliver Kroemer | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Manipulation;Learning for manipulation | https://openreview.net/forum?id=WuBv9-IGDUA | 19 | Multi-Resolution Sensing for Real-Time Control with Vision-Language Models
Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks. Multi-spatial resolution sensing provides hierarchical information captured at different spatial scales and enab... | [
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corl_2023_X0cmlTh1Vl | X0cmlTh1Vl | corl | 2,023 | Waypoint-Based Imitation Learning for Robotic Manipulation | While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. ... | Lucy Xiaoyang Shi;Archit Sharma;Tony Z. Zhao;Chelsea Finn | University of Southern California;Stanford University;Stanford University;Google | Poster | main | imitation learning;waypoints;long-horizon | https://github.com/lucys0/awe | https://openreview.net/forum?id=X0cmlTh1Vl | 59 | Waypoint-Based Imitation Learning for Robotic Manipulation
While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning ... | [
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corl_2023_X7okQlJz9M | X7okQlJz9M | corl | 2,023 | Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control | Seeing-eye robots are very useful tools for guiding visually impaired people, potentially producing a huge societal impact given the low availability and high cost of real guide dogs. Although a few seeing-eye robot systems have already been demonstrated, none considered external tugs from humans, which frequently occu... | David DeFazio;Eisuke Hirota;Shiqi Zhang | State University of New York at Binghamton;New York University;State University of New York at Binghamton | Poster | main | seeing-eye robot;robotic guide dog;human-robot interaction;quadruped locomotion | https://github.com/bu-air-lab/guide_dog | https://openreview.net/forum?id=X7okQlJz9M | 10 | Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control
Seeing-eye robots are very useful tools for guiding visually impaired people, potentially producing a huge societal impact given the low availability and high cost of real guide dogs. Although a few seeing-eye robot systems have already been demon... | [
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corl_2023_XEw-cnNsr6 | XEw-cnNsr6 | corl | 2,023 | DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control | Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present DATT, a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large d... | Kevin Huang;Rwik Rana;Alexander Spitzer;Guanya Shi;Byron Boots | University of Washington;;University of Washington;University of Washington; | Oral | main | Quadrotor;Reinforcement Learning;Adaptive Control | https://github.com/KevinHuang8/DATT | https://openreview.net/forum?id=XEw-cnNsr6 | 27 | DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present DATT, a learning-based approach that can precisely track arbitrary, ... | [
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corl_2023_XMQgwiJ7KSX | XMQgwiJ7KSX | corl | 2,023 | RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefit... | Brianna Zitkovich;Tianhe Yu;Sichun Xu;Peng Xu;Ted Xiao;Fei Xia;Jialin Wu;Paul Wohlhart;Stefan Welker;Ayzaan Wahid;Quan Vuong;Vincent Vanhoucke;Huong Tran;Radu Soricut;Anikait Singh;Jaspiar Singh;Pierre Sermanet;Pannag R Sanketi;Grecia Salazar;Michael S Ryoo;Krista Reymann;Kanishka Rao;Karl Pertsch;Igor Mordatch;Henryk ... | ;Google Brain;;Google;;Google;Google;Graz University of Technology;;Robotics at Google;;Google;;Google;University of California, Berkeley;;Google;Google;;Google DeepMind;;;University of Southern California;;Google DeepMind;Google;Google;;;;;Google;Google;Research, Google;Google DeepMind;Google;;Google;;Research, Google... | Poster | main | vision-language models;robot manipulation;generalization | https://openreview.net/forum?id=XMQgwiJ7KSX | 1,068 | RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained ... | [
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corl_2023_XsWGVbPfB4Z | XsWGVbPfB4Z | corl | 2,023 | 4D-Former: Multimodal 4D Panoptic Segmentation | 4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time. Existing approaches utilize only LiDAR inputs which convey limited information in regions w... | Ali Athar;Enxu Li;Sergio Casas;Raquel Urtasun | Waabi Innovation;Waabi;Department of Computer Science, University of Toronto;University of Toronto | Poster | main | Panoptic Segmentation;Sensor Fusion;Temporal Reasoning;Autonomous Driving | https://openreview.net/forum?id=XsWGVbPfB4Z | 13 | 4D-Former: Multimodal 4D Panoptic Segmentation
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time. Existing approaches utilize only LiDAR input... | [
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corl_2023_ZFjgfJb_5c | ZFjgfJb_5c | corl | 2,023 | Embodied Lifelong Learning for Task and Motion Planning | A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge and proficiency. We formalize this setting with a novel formulation of lifelong learning for ta... | Jorge Mendez-Mendez;Leslie Pack Kaelbling;Tomás Lozano-Pérez | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | task and motion planning;lifelong learning;generative models | https://openreview.net/forum?id=ZFjgfJb_5c | 20 | Embodied Lifelong Learning for Task and Motion Planning
A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge and proficiency. We formalize this sett... | [
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corl_2023__A15qsPswaK | _A15qsPswaK | corl | 2,023 | HYDRA: Hybrid Robot Actions for Imitation Learning | Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations. However, policies learned through IL suffer from state distribution shift at test time, due to compounding errors in action prediction which lead to previously unseen states. Choosing an action representation for the ... | Suneel Belkhale;Yuchen Cui;Dorsa Sadigh | Stanford University;Stanford University;Stanford University | Poster | main | Imitation Learning;Robotics;Manipulation | https://sites.google.com/corp/view/hydra-il-2023 | https://openreview.net/forum?id=_A15qsPswaK | 40 | HYDRA: Hybrid Robot Actions for Imitation Learning
Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations. However, policies learned through IL suffer from state distribution shift at test time, due to compounding errors in action prediction which lead to previously unseen... | [
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corl_2023__DYsYC9smK | _DYsYC9smK | corl | 2,023 | DYNAMO-GRASP: DYNAMics-aware Optimization for GRASP Point Detection in Suction Grippers | In this research, we introduce a novel approach to the challenge of suction grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the grasping process, markedly enhancing the robot's capability to handle previously unseen ob... | Boling Yang;Soofiyan Atar;Markus Grotz;Byron Boots;Joshua Smith | Department of Computer Science, University of Washington;;University of Washington;;University of Washington | Poster | main | Suction Grasping;Manipulation;Deep Learning;Vision | https://github.com/dynamo-grasp/dynamo-grasp | https://openreview.net/forum?id=_DYsYC9smK | 7 | DYNAMO-GRASP: DYNAMics-aware Optimization for GRASP Point Detection in Suction Grippers
In this research, we introduce a novel approach to the challenge of suction grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the gr... | [
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corl_2023__gZLyRGGuo | _gZLyRGGuo | corl | 2,023 | Learning Efficient Abstract Planning Models that Choose What to Predict | An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction of an environment is used to guide low-level decision-making. Recent work has shown how to enable such bilevel planning by learning abstrac... | Nishanth Kumar;Willie McClinton;Rohan Chitnis;Tom Silver;Tomás Lozano-Pérez;Leslie Pack Kaelbling | The AI Institute;Massachusetts Institute of Technology;Meta;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Learning for TAMP;Abstraction Learning;Long-horizon Problems | https://github.com/Learning-and-Intelligent-Systems/predicators_behavior/releases/tag/corl-23-submission | https://openreview.net/forum?id=_gZLyRGGuo | 25 | Learning Efficient Abstract Planning Models that Choose What to Predict
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction of an environment is used to guide low-level decision-making. Recen... | [
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corl_2023__xFJuqBId8c | _xFJuqBId8c | corl | 2,023 | Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement | We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of both scenes and objects, and is trained from demonstrations to operate directly on 3D... | Anthony Simeonov;Ankit Goyal;Lucas Manuelli;Yen-Chen Lin;Alina Sarmiento;Alberto Rodriguez Garcia;Pulkit Agrawal;Dieter Fox | Massachusetts Institute of Technology;NVIDIA;Boston Dynamics;Massachusetts Institute of Technology;;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Department of Computer Science | Poster | main | Object Rearrangement;Multi-modality;Manipulation;Point Clouds;Relations;Diffusion | https://github.com/anthonysimeonov/rpdiff | https://openreview.net/forum?id=_xFJuqBId8c | 46 | Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of ... | [
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corl_2023_a0mFRgadGO | a0mFRgadGO | corl | 2,023 | Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance | We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert supervision, in the form of demonstrations or rich reward functions, to learn long-horizon task... | Jesse Zhang;Jiahui Zhang;Karl Pertsch;Ziyi Liu;Xiang Ren;Minsuk Chang;Shao-Hua Sun;Joseph J Lim | Amazon;University of Southern California;University of Southern California;University of Southern California;University of Southern California;Research, Google;National Taiwan University;Korea Advanced Institute of Science & Technology | Oral | main | Reinforcement Learning;Skill Learning;Large Language Models | https://openreview.net/forum?id=a0mFRgadGO | 80 | Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert super... | [
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corl_2023_afF8RGcBBP | afF8RGcBBP | corl | 2,023 | PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play | Learning from unstructured and uncurated data has become the dominant paradigm for generative approaches in language or vision. Such unstructured and unguided behavior data, commonly known as play, is also easier to collect in robotics but much more difficult to learn from due to its inherently multimodal, noisy, and s... | Lili Chen;Shikhar Bahl;Deepak Pathak | Carnegie Mellon University;Meta Facebook;Carnegie Mellon University | Poster | main | Diffusion Models;Learning from Play;Language-Driven Robotics | https://openreview.net/forum?id=afF8RGcBBP | 53 | PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play
Learning from unstructured and uncurated data has become the dominant paradigm for generative approaches in language or vision. Such unstructured and unguided behavior data, commonly known as play, is also easier to collect in robotics but much mo... | [
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corl_2023_b-cto-fetlz | b-cto-fetlz | corl | 2,023 | HomeRobot: Open-Vocabulary Mobile Manipulation | HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks.
Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational cha... | Sriram Yenamandra;Arun Ramachandran;Karmesh Yadav;Austin S Wang;Mukul Khanna;Theophile Gervet;Tsung-Yen Yang;Vidhi Jain;Alexander Clegg;John M Turner;Zsolt Kira;Manolis Savva;Angel X Chang;Devendra Singh Chaplot;Dhruv Batra;Roozbeh Mottaghi;Yonatan Bisk;Chris Paxton | Georgia Institute of Technology;Georgia Institute of Technology;Meta AI;Meta Facebook;Georgia Institute of Technology;Carnegie Mellon University;Meta AI;Google;Meta AI;;Georgia Tech Research Institute;Simon Fraser University;Simon Fraser University;Georgia Institute of Technology;University of Washington;Meta;Meta Plat... | Poster | main | benchmark;mobile manipulation;sim2real | https://github.com/facebookresearch/home-robot | https://openreview.net/forum?id=b-cto-fetlz | 98 | HomeRobot: Open-Vocabulary Mobile Manipulation
HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks.
Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a... | [
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corl_2023_b1tl3aOt2R2 | b1tl3aOt2R2 | corl | 2,023 | GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields | It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot will need to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we ... | Yanjie Ze;Ge Yan;Yueh-Hua Wu;Annabella Macaluso;Yuying Ge;Jianglong Ye;Nicklas Hansen;Li Erran Li;Xiaolong Wang | Shanghai Jiaotong University;University of California, San Diego;;University of California, San Diego;University of Hong Kong;University of California, San Diego;University of California, San Diego;Columbia University;University of California, San Diego | Oral | main | Robotic Manipulation;Neural Radiance Field;Behavior Cloning | https://github.com/YanjieZe/GNFactor | https://openreview.net/forum?id=b1tl3aOt2R2 | 88 | GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot will need to have a comprehensi... | [
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corl_2023_bIvIUNH9VQ | bIvIUNH9VQ | corl | 2,023 | Hijacking Robot Teams Through Adversarial Communication | Communication is often necessary for robot teams to collaborate and complete a decentralized task. Multi-agent reinforcement learning (MARL) systems allow agents to learn how to collaborate and communicate to complete a task. These domains are ubiquitous and include safety-critical domains such as wildfire fighting, tr... | Zixuan Wu;Sean Charles Ye;Byeolyi Han;Matthew Gombolay | Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology | Oral | main | Adversarial Attacks;Multi-Agent Reinforcement Learning | https://openreview.net/forum?id=bIvIUNH9VQ | 0 | Hijacking Robot Teams Through Adversarial Communication
Communication is often necessary for robot teams to collaborate and complete a decentralized task. Multi-agent reinforcement learning (MARL) systems allow agents to learn how to collaborate and communicate to complete a task. These domains are ubiquitous and inclu... | [
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corl_2023_cjEI5qXoT0 | cjEI5qXoT0 | corl | 2,023 | Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs | We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-based object localization approaches, our system facilitates context-aware entity localization, allo... | Haonan Chang;Kowndinya Boyalakuntla;Shiyang Lu;Siwei Cai;Eric Pu Jing;Shreesh Keskar;Shijie Geng;Adeeb Abbas;Lifeng Zhou;Kostas Bekris;Abdeslam Boularias | Rutgers, New Brunswick;Rutgers University;Rutgers University - New Brunswick;Drexel University;Rutgers University;Rutgers University;ByteDance Inc.;Northeastern University;;Rutgers University;, Rutgers University | Poster | main | Open-Vocabulary Semantic;Scene Graph;Object Grounding | https://github.com/changhaonan/OVSG | https://openreview.net/forum?id=cjEI5qXoT0 | 29 | Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-based object localization approa... | [
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corl_2023_ckeT8cMz_A | ckeT8cMz_A | corl | 2,023 | REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation | Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance forces on the non-prehensile obj... | Zheyuan Hu;Aaron Rovinsky;Jianlan Luo;Vikash Kumar;Abhishek Gupta;Sergey Levine | University of California, Berkeley;University of California, Berkeley;Google;Meta Facebook;University of Washington;Google | Poster | main | Dexterous Manipulation;Reinforcement Learning;Sample-Efficient RL | https://openreview.net/forum?id=ckeT8cMz_A | 11 | REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to d... | [
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corl_2023_dIgCPoy8E3 | dIgCPoy8E3 | corl | 2,023 | Cross-Dataset Sensor Alignment: Making Visual 3D Object Detector Generalizable | While camera-based 3D object detection has evolved rapidly, these models are susceptible to overfitting to specific sensor setups. For example, in autonomous driving, most datasets are collected using a single sensor configuration. This paper evaluates the generalization capability of camera-based 3D object detectors, ... | Liangtao Zheng;Yicheng Liu;Yue Wang;Hang Zhao | Wuhan University;Tsinghua University;NVIDIA;Tsinghua University | Poster | main | 3D object detection;Model Generalization;Autonomous Driving | https://openreview.net/forum?id=dIgCPoy8E3 | 3 | Cross-Dataset Sensor Alignment: Making Visual 3D Object Detector Generalizable
While camera-based 3D object detection has evolved rapidly, these models are susceptible to overfitting to specific sensor setups. For example, in autonomous driving, most datasets are collected using a single sensor configuration. This pape... | [
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corl_2023_dgwvY3H8PAS | dgwvY3H8PAS | corl | 2,023 | Dynamic Handover: Throw and Catch with Bimanual Hands | Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this paper, we design a system with two multi-finger hands... | Binghao Huang;Yuanpei Chen;Tianyu Wang;Yuzhe Qin;Yaodong Yang;Nikolay Atanasov;Xiaolong Wang | University of California, San Diego;South China University of Technology;University of California, San Diego;University of California, San Diego;Peking University;University of California, San Diego;University of California, San Diego | Poster | main | Bimanual Dexterous Manipulation;Sim-to-Real Transfer | https://openreview.net/forum?id=dgwvY3H8PAS | 50 | Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this... | [
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corl_2023_diOr96f65N | diOr96f65N | corl | 2,023 | Quantifying Assistive Robustness Via the Natural-Adversarial Frontier | Our ultimate goal is to build robust policies for robots that assist people. What makes this hard is that people can behave unexpectedly at test time, potentially interacting with the robot outside its training distribution and leading to failures. Even just measuring robustness is a challenge. Adversarial perturbation... | Jerry Zhi-Yang He;Daniel S. Brown;Zackory Erickson;Anca Dragan | ;University of Utah;Carnegie Mellon University;University of California, Berkeley | Poster | main | assistive robots;safety;human-robot interaction;adversarial robustness | https://openreview.net/forum?id=diOr96f65N | 0 | Quantifying Assistive Robustness Via the Natural-Adversarial Frontier
Our ultimate goal is to build robust policies for robots that assist people. What makes this hard is that people can behave unexpectedly at test time, potentially interacting with the robot outside its training distribution and leading to failures. E... | [
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corl_2023_dk-2R1f_LR | dk-2R1f_LR | corl | 2,023 | MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations | Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automatically synthesizing large-scale, rich datasets from only a s... | Ajay Mandlekar;Soroush Nasiriany;Bowen Wen;Iretiayo Akinola;Yashraj Narang;Linxi Fan;Yuke Zhu;Dieter Fox | NVIDIA;University of Texas, Austin;NVIDIA;NVIDIA;NVIDIA;NVIDIA;Computer Science Department, University of Texas, Austin;Department of Computer Science | Poster | main | Imitation Learning;Manipulation | https://github.com/NVlabs/mimicgen_environments | https://openreview.net/forum?id=dk-2R1f_LR | 120 | MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce ... | [
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corl_2023_dxOaNO8bge | dxOaNO8bge | corl | 2,023 | A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots | A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Previous methods are either difficult to continuously improve after the deployment or require a large number of new labels during fine-tuning. M... | Peixin Chang;Shuijing Liu;Tianchen Ji;Neeloy Chakraborty;Kaiwen Hong;Katherine Rose Driggs-Campbell | University of Illinois, Urbana Champaign;University of Illinois, Urbana Champaign;University of Illinois, Urbana Champaign;University of Illinois, Urbana Champaign;UIUC; | Poster | main | Command Following;Multimodal Representation;Reinforcement Learning;Human-in-the-Loop | https://openreview.net/forum?id=dxOaNO8bge | 8 | A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots
A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Previous methods are either difficult to con... | [
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corl_2023_eE3fsO5Mi2 | eE3fsO5Mi2 | corl | 2,023 | Stealthy Terrain-Aware Multi-Agent Active Search | Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. This problem is applicable to reconnaissance tasks w... | Nikhil Angad Bakshi;Jeff Schneider | Carnegie Mellon University;Carnegie Mellon University | Poster | main | Reconnaissance;Adversarial Search;Multi-robot;Active Learning | https://github.com/bakshienator77/Stealthy-Terrain-Aware-Reconnaissance-and-Search | https://openreview.net/forum?id=eE3fsO5Mi2 | 1 | Stealthy Terrain-Aware Multi-Agent Active Search
Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. Thi... | [
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corl_2023_efaE7iJ2GJv | efaE7iJ2GJv | corl | 2,023 | PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation | The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-guided manipulation use 2D image representations, which face difficulties in combining multi-view cameras and inferring precise 3D positions an... | Shizhe Chen;Ricardo Garcia Pinel;Cordelia Schmid;Ivan Laptev | INRIA;INRIA;Inria;INRIA Paris | Poster | main | Robotic manipulation;3D point clouds;language-guided policy | https://openreview.net/forum?id=efaE7iJ2GJv | 37 | PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-guided manipulation use 2D image representations, which face difficulties in... | [
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corl_2023_eyykI3UIHa | eyykI3UIHa | corl | 2,023 | NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities | We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots usi... | Ruohan Zhang;Sharon Lee;Minjune Hwang;Ayano Hiranaka;Chen Wang;Wensi Ai;Jin Jie Ryan Tan;Shreya Gupta;Yilun Hao;Gabrael Levine;Ruohan Gao;Anthony Norcia;Li Fei-Fei;Jiajun Wu | Stanford University;;Stanford University;;Computer Science Department, Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University | Poster | main | Brain-Robot Interface;Human-Robot Interaction | https://openreview.net/forum?id=eyykI3UIHa | 18 | NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans comm... | [
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corl_2023_f55MlAT1Lu | f55MlAT1Lu | corl | 2,023 | BridgeData V2: A Dataset for Robot Learning at Scale | We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 trajectories collected across 24 environments on a publicly available low-cost robot. Unlike many existing robotic manipulation datasets, Br... | Homer Rich Walke;Kevin Black;Tony Z. Zhao;Quan Vuong;Chongyi Zheng;Philippe Hansen-Estruch;Andre Wang He;Vivek Myers;Moo Jin Kim;Max Du;Abraham Lee;Kuan Fang;Chelsea Finn;Sergey Levine | University of California, Berkeley;University of California, Berkeley;Stanford University;;Carnegie Mellon University;;UC Berkeley, University of California, Berkeley;University of California, Berkeley;Stanford University;Stanford University;University of California, Berkeley;;Google;Google | Poster | main | Datasets;Manipulation;Imitation Learning;Offline Reinforcement Learning | https://github.com/rail-berkeley/bridge_data_v2 | https://openreview.net/forum?id=f55MlAT1Lu | 168 | BridgeData V2: A Dataset for Robot Learning at Scale
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 trajectories collected across 24 environments on a publicly available low-cost robot. U... | [
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corl_2023_fNLBmtyBiC | fNLBmtyBiC | corl | 2,023 | A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling | Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a simulation-based framework for a) predicting ways in which an autonomous system is likely t... | Charles Dawson;Chuchu Fan | Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Automatic design tools;root-cause failure analysis;optimization-as-inference | https://github.com/MIT-REALM/architect_corl_23 | https://openreview.net/forum?id=fNLBmtyBiC | 11 | A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, ... | [
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corl_2023_fSmkKmWM5Ry | fSmkKmWM5Ry | corl | 2,023 | Stochastic Occupancy Grid Map Prediction in Dynamic Scenes | This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a range of possible future states of the environment. The algorithm tak... | Zhanteng Xie;Philip Dames | Temple University;Temple University | Poster | main | Environment Prediction;Probabilistic Inference;Robot Learning | https://github.com/TempleRAIL/SOGMP | https://openreview.net/forum?id=fSmkKmWM5Ry | 5 | Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a range of p... | [
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corl_2023_fa7FzDjhzs9 | fa7FzDjhzs9 | corl | 2,023 | HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation | Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in reasoning about gripper-object interactions. In this work, we introduce ... | Wenxuan Zhou;Bowen Jiang;Fan Yang;Chris Paxton;David Held | Meta AI;Carnegie Mellon University;Carnegie Mellon University;Meta Platforms;Carnegie Mellon University | Oral | main | Action Representation;Reinforcement Learning with 3D Vision;Non-prehensile Manipulation | https://openreview.net/forum?id=fa7FzDjhzs9 | 22 | HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation
Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenge... | [
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corl_2023_flyQ0v8cgC | flyQ0v8cgC | corl | 2,023 | Continual Vision-based Reinforcement Learning with Group Symmetries | Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks. However, current continual RL approaches overlook the fact that certain tasks are identical under basic group operat... | Shiqi Liu;Mengdi Xu;Peide Huang;Xilun Zhang;Yongkang Liu;Kentaro Oguchi;Ding Zhao | ;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Toyota;Toyota Motor North America;Carnegie Mellon University | Oral | main | Continual Learning;Symmetry;Manipulation | https://openreview.net/forum?id=flyQ0v8cgC | 10 | Continual Vision-based Reinforcement Learning with Group Symmetries
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks. However, current continual RL approaches overloo... | [
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corl_2023_fvXFBCHVGn | fvXFBCHVGn | corl | 2,023 | Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation | Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free feedback control, multi-agent wheel-to-wheel racing poses several challenges including planning over u... | Peter Werner;Tim Seyde;Paul Drews;Thomas Matrai Balch;Igor Gilitschenski;Wilko Schwarting;Guy Rosman;Sertac Karaman;Daniela Rus | Computer Science and Artificial Intelligence Laboratory, Electrical Engineering & Computer Science;Massachusetts Institute of Technology;Toyota Research Institute;Toyota Research Institute;University of Toronto;;Toyota Research Institute;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Multi-Agent;Reinforcement Learning;Sim-to-Real Transfer;Autonomous Racing | https://openreview.net/forum?id=fvXFBCHVGn | 6 | Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation
Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free fe... | [
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corl_2023_fviZhMCr62 | fviZhMCr62 | corl | 2,023 | Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation | Humans have the remarkable ability to navigate through unfamiliar environments by solely relying on our prior knowledge and descriptions of the environment. For robots to perform the same type of navigation, they need to be able to associate natural language descriptions with their associated physical environment with ... | Harel Biggie;Ajay Narasimha Mopidevi;Dusty Woods;Chris Heckman | University of Colorado at Boulder;University of Colorado at Boulder;;University of Colorado at Boulder | Poster | main | Natural language;navigation;contextual navigation | https://github.com/arpg/navcon | https://openreview.net/forum?id=fviZhMCr62 | 13 | Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation
Humans have the remarkable ability to navigate through unfamiliar environments by solely relying on our prior knowledge and descriptions of the environment. For robots to perform the same type of navigation, they need to be able to ... | [
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corl_2023_gFXVysXh48K | gFXVysXh48K | corl | 2,023 | Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning | Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning these skills is challenging due to data inefficiency in the real... | Xiang Zhang;Changhao Wang;Lingfeng Sun;Zheng Wu;Xinghao Zhu;Masayoshi Tomizuka | University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;;University of California, Berkeley | Poster | main | Contact-rich Manipulation;Compliance Control | https://openreview.net/forum?id=gFXVysXh48K | 24 | Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning
Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable... | [
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corl_2023_gVBvtRqU1_ | gVBvtRqU1_ | corl | 2,023 | OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data | This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training. Given a language query, the proposed method is able to return a ranked set of 3D object instance segments based on the feature similarity of the instance and the te... | Shiyang Lu;Haonan Chang;Eric Pu Jing;Abdeslam Boularias;Kostas Bekris | Rutgers University - New Brunswick;Rutgers, New Brunswick;Rutgers University;, Rutgers University;Rutgers University | Poster | main | Open-Vocabulary;3D Instance Retrieval | https://github.com/shiyoung77/OVIR-3D/ | https://openreview.net/forum?id=gVBvtRqU1_ | 61 | OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training. Given a language query, the proposed method is able to return a ranked set of 3D object i... | [
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corl_2023_g_PPHV_GkX | g_PPHV_GkX | corl | 2,023 | Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model | This work considers planning the manipulation of deformable 1-dimensional objects, such as ropes or cables, specifically to tie knots. We propose TWISTED: Tying With Inverse model and Search in Topological space Excluding Demos, a hierarchical planning approach which, at the high level, uses ideas from knot-theory to p... | Matan Sudry;Tom Jurgenson;Aviv Tamar;Erez Karpas | Technion, Technion;Technion;Technion, Technion;Technion - Israel Institute of Technology, Technion | Poster | main | Manipulation;Robot Learning and Planning | https://openreview.net/forum?id=g_PPHV_GkX | 4 | Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model
This work considers planning the manipulation of deformable 1-dimensional objects, such as ropes or cables, specifically to tie knots. We propose TWISTED: Tying With Inverse model and Search in Topological space Excluding Demos, a... | [
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corl_2023_gdkKi_F55h | gdkKi_F55h | corl | 2,023 | SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects | To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a ... | Ning Gao;Vien Anh Ngo;Hanna Ziesche;Gerhard Neumann | Robert Bosch GmbH, Bosch;Bosch Center for Artificial Intelligence;Karlsruhe Institute of Technology;Robert Bosch GmbH, Bosch | Poster | main | https://openreview.net/forum?id=gdkKi_F55h | 6 | SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuo... | [
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corl_2023_h-geaPzuJu | h-geaPzuJu | corl | 2,023 | DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation | Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-error learning is infeasible or specifying a cost function is difficult, such as robotic surgery, autonomous driving, and path-finding for NASA's Mars rovers. However, two key problems remain challenging in OLfD: 1) heterogeneity: demons... | Sravan Jayanthi;Letian Chen;Nadya Balabanska;Van Duong;Erik Scarlatescu;Ezra Ameperosa;Zulfiqar Haider Zaidi;Daniel Martin;Taylor Keith Del Matto;Masahiro Ono;Matthew Gombolay | Georgia Institute of Technology;Toyota Research Institute;Jet Propulsion Laboratory;;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Jet Propulsion Laboratory;Georgia Institute of Technology | Poster | main | Learning from Heterogeneous Demonstration;Network Distillation;Offline Imitation Learning | https://openreview.net/forum?id=h-geaPzuJu | 5 | DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation
Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-error learning is infeasible or specifying a cost function is difficult, such as robotic surgery, autonomous driving, and path-finding for NASA's Ma... | [
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corl_2023_h8halpbqB- | h8halpbqB- | corl | 2,023 | Im2Contact: Vision-Based Contact Localization Without Touch or Force Sensing | Contacts play a critical role in most manipulation tasks. Robots today mainly use proximal touch/force sensors to sense contacts, but the information they provide must be calibrated and is inherently local, with practical applications relying either on extensive surface coverage or restrictive assumptions to resolve am... | Leon Kim;Yunshuang Li;Michael Posa;Dinesh Jayaraman | University of Pennsylvania;University of Pennsylvania;University of Pennsylvania;University of Pennsylvania | Poster | main | contact perception;manipulation;vision-based | https://openreview.net/forum?id=h8halpbqB- | 4 | Im2Contact: Vision-Based Contact Localization Without Touch or Force Sensing
Contacts play a critical role in most manipulation tasks. Robots today mainly use proximal touch/force sensors to sense contacts, but the information they provide must be calibrated and is inherently local, with practical applications relying ... | [
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corl_2023_hRZ1YjDZmTo | hRZ1YjDZmTo | corl | 2,023 | MimicPlay: Long-Horizon Imitation Learning by Watching Human Play | Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data - video sequences of peo... | Chen Wang;Linxi Fan;Jiankai Sun;Ruohan Zhang;Li Fei-Fei;Danfei Xu;Yuke Zhu;Anima Anandkumar | Computer Science Department, Stanford University;NVIDIA;Stanford University;Stanford University;Stanford University;NVIDIA;Computer Science Department, University of Texas, Austin;California Institute of Technology | Oral | main | Imitation Learning;Learning from Human;Long-Horizon Manipulation | https://github.com/j96w/MimicPlay | https://openreview.net/forum?id=hRZ1YjDZmTo | 187 | MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data ... | [
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corl_2023_i84V7i6KEMd | i84V7i6KEMd | corl | 2,023 | Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards | Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude. In our experiments we ... | Katherine Metcalf;Miguel Sarabia;Natalie Mackraz;Barry-John Theobald | ;;Apple;Apple | Poster | main | human-in-the-loop learning;preference-based RL;RLHF | https://openreview.net/forum?id=i84V7i6KEMd | 7 | Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function im... | [
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corl_2023_ihqTtzS83VS | ihqTtzS83VS | corl | 2,023 | Learning Reusable Manipulation Strategies | Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categories (e.g., forks and hammers). Additionally... | Jiayuan Mao;Tomás Lozano-Pérez;Joshua B. Tenenbaum;Leslie Pack Kaelbling | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Contact Modeling and Manipulation;Task and Motion Planning | https://openreview.net/forum?id=ihqTtzS83VS | 11 | Learning Reusable Manipulation Strategies
Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categori... | [
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corl_2023_j2AQ-WJ_ze | j2AQ-WJ_ze | corl | 2,023 | Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter | Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, which predicts a grasp pose for an object referred through natural language in cl... | Georgios Tziafas;Yucheng XU;Arushi Goel;Mohammadreza Kasaei;Zhibin Li;Hamidreza Kasaei | University of Groningen;University of Edinburgh, University of Edinburgh;University of Edinburgh;University of Edinburgh, University of Edinburgh;University College London, University of London;University of Groningen | Poster | main | Language-Guided Robot Grasping;Referring Grasp Synthesis;Visual Grounding | https://github.com/gtziafas/OCID-VLG | https://openreview.net/forum?id=j2AQ-WJ_ze | 27 | Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter
Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, w... | [
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corl_2023_k-Fg8JDQmc | k-Fg8JDQmc | corl | 2,023 | Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping | Grasping objects by a specific subpart is often crucial for safety and for executing downstream tasks. We propose LERF-TOGO, Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which uses vision-language models zero-shot to output a grasp distribution over an object given a natural language query. ... | Adam Rashid;Satvik Sharma;Chung Min Kim;Justin Kerr;Lawrence Yunliang Chen;Angjoo Kanazawa;Ken Goldberg | ;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley | Oral | main | NeRF;Natural Language;Grasping;Semantics | https://openreview.net/forum?id=k-Fg8JDQmc | 88 | Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping
Grasping objects by a specific subpart is often crucial for safety and for executing downstream tasks. We propose LERF-TOGO, Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which uses vision-language models zero-shot to outp... | [
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corl_2023_kOm3jWX8YN | kOm3jWX8YN | corl | 2,023 | Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning | Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the suboptimal nature of some demonstrations, which can compromise the overall dataset quality and hence the learning o... | Sachit Kuhar;Shuo Cheng;Shivang Chopra;Matthew Bronars;Danfei Xu | Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;NVIDIA | Poster | main | Imitation Learning;Preference Learning;Manipulation | https://openreview.net/forum?id=kOm3jWX8YN | 9 | Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the subop... | [
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corl_2023_kSXh83gWWy | kSXh83gWWy | corl | 2,023 | Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area | Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment, which is updated online along the way, instead of a prior map of the environment. Inspired by the recent developments in deep reinforcement learning (DRL... | Jingsong Liang;Zhichen Wang;Yuhong Cao;Jimmy Chiun;Mengqi Zhang;Guillaume Adrien Sartoretti | National University of Singapore, NUS;National University of Singapore;National University of Singapore;National University of Singapore;National University of Singapore;National University of Singapore | Poster | main | deep reinforcement learning;mapless navigation;context-aware decision-making | https://github.com/marmotlab/Context_Aware_Navigation | https://openreview.net/forum?id=kSXh83gWWy | 10 | Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area
Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment, which is updated online along the way, instead of a prior map... | [
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corl_2023_keAPCON4jHC | keAPCON4jHC | corl | 2,023 | Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization | Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations, which excessively restricts its application for real-world robotics. Prior work claimed that adding regularization to the value function is equivalent to learning a robust policy under uncertain transit... | Yuan Zhang;Jianhong Wang;Joschka Boedecker | University of Freiburg;Imperial College London;Universität Freiburg | Poster | main | Reinforcement Learning;Robustness;Continuous Control;Robotics | github.com/mikezhang95/rrl_usr | https://openreview.net/forum?id=keAPCON4jHC | 5 | Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization
Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations, which excessively restricts its application for real-world robotics. Prior work claimed that adding regulari... | [
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corl_2023_low-53sFqn | low-53sFqn | corl | 2,023 | Fleet Active Learning: A Submodular Maximization Approach | In multi-robot systems, robots often gather data to improve the performance of their deep neural networks (DNNs) for perception and planning. Ideally, these robots should select the most informative samples from their local data distributions by employing active learning approaches. However, when the data collection is... | Oguzhan Akcin;Orhan Unuvar;Onat Ure;Sandeep P. Chinchali | The University of Texas at Austin;University of Texas at Austin;University of Texas at Austin;University of Texas at Austin | Poster | main | Active Learning;Cloud Robotics;Robotic Perception | https://github.com/UTAustin-SwarmLab/Fleet-Active-Learning.git | https://openreview.net/forum?id=low-53sFqn | 5 | Fleet Active Learning: A Submodular Maximization Approach
In multi-robot systems, robots often gather data to improve the performance of their deep neural networks (DNNs) for perception and planning. Ideally, these robots should select the most informative samples from their local data distributions by employing active... | [
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corl_2023_mTZcxs2O7k | mTZcxs2O7k | corl | 2,023 | Batch Differentiable Pose Refinement for In-The-Wild Camera/LiDAR Extrinsic Calibration | Accurate camera to LiDAR (Light Detection and Ranging) extrinsic calibration is important for robotic tasks carrying out tight sensor fusion --- such as target tracking and odometry. Calibration is typically performed before deployment in controlled conditions using calibration targets, however, this limits scalability... | Lanke Frank Tarimo Fu;Maurice Fallon | University of Oxford;University of Oxford | Poster | main | Sensor Fusion;Extrinsic Calibration;Differentiable Optimization | https://openreview.net/forum?id=mTZcxs2O7k | 6 | Batch Differentiable Pose Refinement for In-The-Wild Camera/LiDAR Extrinsic Calibration
Accurate camera to LiDAR (Light Detection and Ranging) extrinsic calibration is important for robotic tasks carrying out tight sensor fusion --- such as target tracking and odometry. Calibration is typically performed before deploym... | [
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corl_2023_n9lew97SAn | n9lew97SAn | corl | 2,023 | Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning | The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other methods for mitigating distributional shifts have made offline reinforcement lea... | Jianlan Luo;Perry Dong;Jeffrey Wu;Aviral Kumar;Xinyang Geng;Sergey Levine | Google;;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Google | Poster | main | Offline Reinforcement Learning;Discretization;Robot Skill Learning | https://openreview.net/forum?id=n9lew97SAn | 25 | Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning
The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other metho... | [
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corl_2023_nKWQnYkkwX | nKWQnYkkwX | corl | 2,023 | Language-Guided Traffic Simulation via Scene-Level Diffusion | Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are difficult for practitioners to use. To remedy this, we present CT... | Ziyuan Zhong;Davis Rempe;Yuxiao Chen;Boris Ivanovic;Yulong Cao;Danfei Xu;Marco Pavone;Baishakhi Ray | Columbia University;Stanford University;NVIDIA;NVIDIA;NVIDIA;Stanford University;Columbia University;California Institute of Technology | Oral | main | Traffic Simulation;Multi-Agent Diffusion;Large Language Model | https://openreview.net/forum?id=nKWQnYkkwX | 94 | Language-Guided Traffic Simulation via Scene-Level Diffusion
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are diff... | [
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corl_2023_nNsZxc2cmO | nNsZxc2cmO | corl | 2,023 | FindThis: Language-Driven Object Disambiguation in Indoor Environments | Natural language is naturally ambiguous. In this work, we consider interactions between a user and a mobile service robot tasked with locating a desired object, specified by a language utterance. We present a task FindThis, which addresses the problem of how to disambiguate and locate the particular object instance des... | Arjun Majumdar;Fei Xia;brian ichter;Dhruv Batra;Leonidas Guibas | Georgia Institute of Technology;Google;Google;Georgia Institute of Technology;Stanford University | Poster | main | object disambiguation;instruction following;language interaction;visual navigation | https://openreview.net/forum?id=nNsZxc2cmO | 11 | FindThis: Language-Driven Object Disambiguation in Indoor Environments
Natural language is naturally ambiguous. In this work, we consider interactions between a user and a mobile service robot tasked with locating a desired object, specified by a language utterance. We present a task FindThis, which addresses the probl... | [
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corl_2023_nyY6UgXYyfF | nyY6UgXYyfF | corl | 2,023 | Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation | Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synthetic and real scenarios and to verify good performance. However, they primarily only test the motio... | Jay Sarva;Jingkang Wang;James Tu;Yuwen Xiong;Sivabalan Manivasagam;Raquel Urtasun | Brown University;University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;;Department of Computer Science, University of Toronto | Poster | main | closed-loop simulation;adversarial robustness;self-driving | https://openreview.net/forum?id=nyY6UgXYyfF | 7 | Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synthetic and real scenarios... | [
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corl_2023_o-K3HVUeEw | o-K3HVUeEw | corl | 2,023 | Composable Part-Based Manipulation | In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the functional correspondences between object parts, we conceptualize function... | Weiyu Liu;Jiayuan Mao;Joy Hsu;Tucker Hermans;Animesh Garg;Jiajun Wu | Stanford University;Massachusetts Institute of Technology;Stanford University;University of Utah;University of Toronto;Stanford University | Poster | main | Manipulation;Part Decomposition;Diffusion Model | https://cpmcorl2023.github.io/ | https://openreview.net/forum?id=o-K3HVUeEw | 14 | Composable Part-Based Manipulation
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the functional correspondences between obje... | [
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corl_2023_o2wNSCTkq0 | o2wNSCTkq0 | corl | 2,023 | Learning Sequential Acquisition Policies for Robot-Assisted Feeding | A robot providing mealtime assistance must perform specialized maneuvers with various utensils in order to pick up and feed a range of food items. Beyond these dexterous low-level skills, an assistive robot must also plan these strategies in sequence over a long horizon to clear a plate and complete a meal. Previous me... | Priya Sundaresan;Jiajun Wu;Dorsa Sadigh | Stanford University;Stanford University;Stanford University | Poster | main | Deformable Manipulation;Dexterous Manipulation | https://sites.google.com/view/vaporsbot | https://openreview.net/forum?id=o2wNSCTkq0 | 10 | Learning Sequential Acquisition Policies for Robot-Assisted Feeding
A robot providing mealtime assistance must perform specialized maneuvers with various utensils in order to pick up and feed a range of food items. Beyond these dexterous low-level skills, an assistive robot must also plan these strategies in sequence o... | [
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corl_2023_o82EXEK5hu6 | o82EXEK5hu6 | corl | 2,023 | Parting with Misconceptions about Learning-based Vehicle Motion Planning | The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these ... | Daniel Dauner;Marcel Hallgarten;Andreas Geiger;Kashyap Chitta | Eberhard-Karls-Universität Tübingen;Eberhard-Karls-Universität Tübingen;University of Tuebingen;University of Tübingen | Poster | main | Motion Planning;Autonomous Driving;Data-driven Simulation | https://github.com/autonomousvision/tuplan_garage | https://openreview.net/forum?id=o82EXEK5hu6 | 141 | Parting with Misconceptions about Learning-based Vehicle Motion Planning
The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems strug... | [
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corl_2023_oqOfLP6bJy | oqOfLP6bJy | corl | 2,023 | Contrastive Value Learning: Implicit Models for Simple Offline RL | Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. While conventional model-based methods learn a 1-step model, predicting the immediate next state, these methods must be p... | Bogdan Mazoure;Benjamin Eysenbach;Ofir Nachum;Jonathan Tompson | Apple;Carnegie Mellon University;OpenAI;Google DeepMind | Poster | main | reinforcement learning;robotics;metaworld;unsupervised learning;contrastive learning;noise-contrastive estimation;generative model | https://openreview.net/forum?id=oqOfLP6bJy | 11 | Contrastive Value Learning: Implicit Models for Simple Offline RL
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. While conventional model-based methods learn a 1-step m... | [
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corl_2023_oyWkrG-LD5 | oyWkrG-LD5 | corl | 2,023 | Geometry Matching for Multi-Embodiment Grasping | While significant progress has been made on the problem of generating grasps, many existing learning-based approaches still concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of grasp modes. In this paper, we tackle the problem of grasping mul... | Maria Attarian;Muhammad Adil Asif;Jingzhou Liu;Ruthrash Hari;Animesh Garg;Igor Gilitschenski;Jonathan Tompson | Google;University of Toronto;University of Toronto;;University of Toronto;University of Toronto;Google DeepMind | Poster | main | Multi-Embodiment;Dexterous Grasping;Graph Neural Networks | https://openreview.net/forum?id=oyWkrG-LD5 | 9 | Geometry Matching for Multi-Embodiment Grasping
While significant progress has been made on the problem of generating grasps, many existing learning-based approaches still concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of grasp modes. In t... | [
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corl_2023_pLCQkMojXI | pLCQkMojXI | corl | 2,023 | Rearrangement Planning for General Part Assembly | Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise p... | Yulong Li;Andy Zeng;Shuran Song | Columbia University;Columbia University;Google | Oral | main | robotic assembly;pose estimation;3D perception | https://github.com/real-stanford/gpat | https://openreview.net/forum?id=pLCQkMojXI | 11 | Rearrangement Planning for General Part Assembly
Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system... | [
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corl_2023_psLlVbTFBua | psLlVbTFBua | corl | 2,023 | FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection | Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interaction, after training on other articulated objects. Previous approaches for articulated object... | Harry Zhang;Ben Eisner;David Held | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Articulated objects manipulation;representation learning | https://sites.google.com/view/flowbotpp/home | https://openreview.net/forum?id=psLlVbTFBua | 35 | FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection
Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interact... | [
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corl_2023_psyvs5wdAV | psyvs5wdAV | corl | 2,023 | Equivariant Motion Manifold Primitives | Existing movement primitive models for the most part focus on representing and generating a single trajectory for a given task, limiting their adaptability to situations in which unforeseen obstacles or new constraints may arise. In this work we propose Motion Manifold Primitives (MMP), a movement primitive paradigm th... | Byeongho Lee;Yonghyeon Lee;Seungyeon Kim;MinJun Son;Frank C. Park | ;Seoul National University;Seoul National University;Seoul National University;Seoul National University | Poster | main | Movement primitives;Manifold;LfD;Equivariance | https://github.com/dlsfldl/EMMP-public | https://openreview.net/forum?id=psyvs5wdAV | 9 | Equivariant Motion Manifold Primitives
Existing movement primitive models for the most part focus on representing and generating a single trajectory for a given task, limiting their adaptability to situations in which unforeseen obstacles or new constraints may arise. In this work we propose Motion Manifold Primitives ... | [
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corl_2023_pw-OTIYrGa | pw-OTIYrGa | corl | 2,023 | On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills | Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, ... | Yunhai Han;Mandy Xie;Ye Zhao;Harish Ravichandar | Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology | Oral | main | Koopman Operator;Dexterous Manipulation | https://github.com/GT-STAR-Lab/KODex | https://openreview.net/forum?id=pw-OTIYrGa | 16 | On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant ... | [
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corl_2023_q0VAoefCI2 | q0VAoefCI2 | corl | 2,023 | Task-Oriented Koopman-Based Control with Contrastive Encoder | We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prioritizing the task cost as the main objective for controller learning, w... | Xubo Lyu;Hanyang Hu;Seth Siriya;Ye Pu;Mo Chen | Simon Fraser University;Simon Fraser University;University of Melbourne;University of Melbourne;Simon Fraser University | Oral | main | Learning and control;Koopman-based control;Represention learning | https://sites.google.com/view/kpmlilatsupp/ | https://openreview.net/forum?id=q0VAoefCI2 | 6 | Task-Oriented Koopman-Based Control with Contrastive Encoder
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prioritizing t... | [
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corl_2023_qVc7NWYTRZ6 | qVc7NWYTRZ6 | corl | 2,023 | An Unbiased Look at Datasets for Visuo-Motor Pre-Training | Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by pre-training visual representations on large-scale but out-of-domain data (e.g., videos of egocentric interactions) and then transferring th... | Sudeep Dasari;Mohan Kumar Srirama;Unnat Jain;Abhinav Gupta | ;Carnegie Mellon University;;Carnegie Mellon University | Poster | main | Visual Representation Learning;Datasets;Manipulation | https://github.com/SudeepDasari/data4robotics | https://openreview.net/forum?id=qVc7NWYTRZ6 | 37 | An Unbiased Look at Datasets for Visuo-Motor Pre-Training
Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by pre-training visual representations on large-scale but out-of-domain data (e.g., v... | [
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corl_2023_rOCWUmMBSnH | rOCWUmMBSnH | corl | 2,023 | A Policy Optimization Method Towards Optimal-time Stability | In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. ... | Shengjie Wang;Lan Fengb;Xiang Zheng;Yuxue Cao;Oluwatosin OluwaPelumi Oseni;Haotian Xu;Tao Zhang;Yang Gao | Tsinghua University;Tsinghua University;;;;;;Tsinghua University | Poster | main | Reinforcement Learning;Robotic Control;Stability | https://openreview.net/forum?id=rOCWUmMBSnH | 3 | A Policy Optimization Method Towards Optimal-time Stability
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equi... | [
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corl_2023_rPye6EZxmI | rPye6EZxmI | corl | 2,023 | Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms | Control policies for soft robot arms typically assume quasi-static motion or require a hand-designed motion plan. To achieve real-time planning and control for tasks requiring highly dynamic maneuvers, we apply deep reinforcement learning to train a policy entirely in simulation, and we identify strategies and insights... | Rianna Jitosho;Tyler Ga Wei Lum;Allison Okamura;Karen Liu | Stanford University;Stanford University;;Computer Science Department, Stanford University | Poster | main | Soft Robotics;Reinforcement Learning;Sim-to-Real Transfer;Dynamics and Control | https://github.com/tylerlum/Vine_Robot_IsaacGymEnvs | https://openreview.net/forum?id=rPye6EZxmI | 13 | Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms
Control policies for soft robot arms typically assume quasi-static motion or require a hand-designed motion plan. To achieve real-time planning and control for tasks requiring highly dynamic maneuvers, we apply deep rei... | [
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corl_2023_rYZBdBytxBx | rYZBdBytxBx | corl | 2,023 | HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs | Robots are becoming increasingly integrated into our lives, assisting us in various tasks. To ensure effective collaboration between humans and robots, it is essential that they understand our intentions and anticipate our actions. In this paper, we propose a Human-Object Interaction (HOI) anticipation framework for co... | Esteve Valls Mascaro;Daniel Sliwowski;Dongheui Lee | Technische Universität Wien;Technische Universität Wien;Technische Universität Wien | Poster | main | Human-Object Interaction;Human-Robot Collaboration;Human Intention | https://openreview.net/forum?id=rYZBdBytxBx | 10 | HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
Robots are becoming increasingly integrated into our lives, assisting us in various tasks. To ensure effective collaboration between humans and robots, it is essential that they understand our intentions and anticipate our ... | [
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corl_2023_rpWi4SYGXj | rpWi4SYGXj | corl | 2,023 | Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments | Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to... | Jason Xinyu Liu;Ziyi Yang;Ifrah Idrees;Sam Liang;Benjamin Schornstein;Stefanie Tellex;Ankit Shah | Brown University;Brown University;Brown University;;Brown University;, Brown University;Brown University | Poster | main | language grounding;temporal reasoning;robot navigation;formal methods | https://github.com/h2r/Lang2LTL | https://openreview.net/forum?id=rpWi4SYGXj | 46 | Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments
Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training dat... | [
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corl_2023_rvh0vkwKUM | rvh0vkwKUM | corl | 2,023 | Predicting Routine Object Usage for Proactive Robot Assistance | Proactivity in robot assistance refers to the robot's ability to anticipate user needs and perform assistive actions without explicit requests. This requires understanding user routines, predicting consistent activities, and actively seeking information to predict inconsistent behaviors. We propose SLaTe-PRO (Sequentia... | Maithili Patel;Aswin Gururaj Prakash;Sonia Chernova | Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology | Poster | main | Proactive Robot Assistance;User Routine Understanding;Interactive Clarification;Robot Learning | https://github.com/Maithili/SLaTe-PRO | https://openreview.net/forum?id=rvh0vkwKUM | 10 | Predicting Routine Object Usage for Proactive Robot Assistance
Proactivity in robot assistance refers to the robot's ability to anticipate user needs and perform assistive actions without explicit requests. This requires understanding user routines, predicting consistent activities, and actively seeking information to ... | [
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corl_2023_rxlokRzNWRq | rxlokRzNWRq | corl | 2,023 | ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting | Seamless human-robot manipulation in close proximity relies on accurate forecasts of human motion. While there has been significant progress in learning forecast models at scale, when applied to manipulation tasks, these models accrue high errors at critical transition points leading to degradation in downstream planni... | Kushal Kedia;Prithwish Dan;Atiksh Bhardwaj;Sanjiban Choudhury | Cornell University;Department of Computer Science, Cornell University;Cornell University;Cornell University | Poster | main | Collaborative Manipulation;Forecasting;Model Predictive Control | https://openreview.net/forum?id=rxlokRzNWRq | 5 | ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting
Seamless human-robot manipulation in close proximity relies on accurate forecasts of human motion. While there has been significant progress in learning forecast models at scale, when applied to manipulation tasks, these models accrue high errors at... | [
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corl_2023_sLhk0keeiseH | sLhk0keeiseH | corl | 2,023 | That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation | Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual and tactile sensing. However, pure vision often fails to capture high-frequency interaction, while current tactile sensors can be too delicate for... | Abitha Thankaraj;Lerrel Pinto | ;New York University | Poster | main | Dynamic manipulation;Self supervised learning;Audio | https://github.com/abitha-thankaraj/audio-robot-learning | https://openreview.net/forum?id=sLhk0keeiseH | 12 | That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation
Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual and tactile sensing. However, pure vision often fails to capture high-... | [
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corl_2023_uJqxFjF1xWp | uJqxFjF1xWp | corl | 2,023 | BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities | Collaborative perception enables agents to share complementary perceptual information with nearby agents. This can significantly benefit the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. Most proposed approaches mainly focus on single modality (especially LiD... | Binyu Zhao;Wei ZHANG;Zhaonian Zou | Harbin Institute of Technology;Harbin Institute of Technology;Harbin Institute of Technology | Poster | main | Multi-Agent Perception;Multi-Modal Fusion;Vehicle-to-Everything (V2X) Application | https://openreview.net/forum?id=uJqxFjF1xWp | 11 | BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities
Collaborative perception enables agents to share complementary perceptual information with nearby agents. This can significantly benefit the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. M... | [
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corl_2023_uo937r5eTE | uo937r5eTE | corl | 2,023 | Robot Parkour Learning | Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, \textit{auton... | Ziwen Zhuang;Zipeng Fu;Jianren Wang;Christopher G Atkeson;Sören Schwertfeger;Chelsea Finn;Hang Zhao | ShanghaiTech University;Stanford University;Carnegie Mellon University;;;Google;Tsinghua University | Oral | main | Agile Locomotion;End-to-End Vision-Based Control;Sim-to-Real | https://github.com/ZiwenZhuang/parkour | https://openreview.net/forum?id=uo937r5eTE | 195 | Robot Parkour Learning
Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards.... | [
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corl_2023_veGdf4L4Xz | veGdf4L4Xz | corl | 2,023 | KITE: Keypoint-Conditioned Policies for Semantic Manipulation | While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation. A crucial step to realizing a performant instruction-following robot is achieving semantic manipulation – where a robot interpret... | Priya Sundaresan;Suneel Belkhale;Dorsa Sadigh;Jeannette Bohg | Stanford University;Stanford University;Stanford University;Stanford University | Poster | main | Semantic Manipulation;Language Grounding;Keypoint Perception | http://tinyurl.com/kite-site | https://openreview.net/forum?id=veGdf4L4Xz | 25 | KITE: Keypoint-Conditioned Policies for Semantic Manipulation
While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation. A crucial step to realizing a performant instruction-following robo... | [
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corl_2023_vsEWu6mMUhB | vsEWu6mMUhB | corl | 2,023 | Semantic Mechanical Search with Large Vision and Language Models | Moving objects to find a fully-occluded target object, known as mechanical search, is a challenging problem in robotics. As objects are often organized semantically, we conjecture that semantic information about object relationships can facilitate mechanical search and reduce search time. Large pretrained vision and la... | Satvik Sharma;Huang Huang;Kaushik Shivakumar;Lawrence Yunliang Chen;Ryan Hoque;brian ichter;Ken Goldberg | University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Google;University of California, Berkeley | Poster | main | Vision and Language Models in Robotics;Mechanical Search;Object search | https://openreview.net/forum?id=vsEWu6mMUhB | 11 | Semantic Mechanical Search with Large Vision and Language Models
Moving objects to find a fully-occluded target object, known as mechanical search, is a challenging problem in robotics. As objects are often organized semantically, we conjecture that semantic information about object relationships can facilitate mechani... | [
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corl_2023_w5ONmpgnfG | w5ONmpgnfG | corl | 2,023 | One-Shot Imitation Learning: A Pose Estimation Perspective | In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as a combination of trajectory transfer and unseen object pos... | Pietro Vitiello;Kamil Dreczkowski;Edward Johns | Imperial College London;Imperial College London;Imperial College London | Poster | main | One-Shot Imitation Learning;Unseen Object Pose Estimation;Robot Manipulation | https://openreview.net/forum?id=w5ONmpgnfG | 23 | One-Shot Imitation Learning: A Pose Estimation Perspective
In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as... | [
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corl_2023_wH23nZpVTF6 | wH23nZpVTF6 | corl | 2,023 | DEFT: Dexterous Fine-Tuning for Hand Policies | Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. Alth... | Aditya Kannan;Kenneth Shaw;Shikhar Bahl;Pragna Mannam;Deepak Pathak | School of Computer Science, Carnegie Mellon University;Carnegie Mellon University;Meta Facebook;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Dexterous Manipulation;Reinforcement Learning;Learning from Videos | https://openreview.net/forum?id=wH23nZpVTF6 | -1 | DEFT: Dexterous Fine-Tuning for Hand Policies
Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well a... | [
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corl_2023_wMpOMO0Ss7a | wMpOMO0Ss7a | corl | 2,023 | SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning | Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a significant challenge for robotics. We introduce SayPlan, a scalable approach to LLM-based, large... | Krishan Rana;Jesse Haviland;Sourav Garg;Jad Abou-Chakra;Ian Reid;Niko Suenderhauf | Queensland University of Technology;Queensland University of Technology;Queensland University of Technology;Queensland University of Technology;University of Adelaide;Queensland University of Technology | Oral | main | robot task planning;large language models;semantic search;LLM-based planning;3D scene graphs | https://openreview.net/forum?id=wMpOMO0Ss7a | 315 | SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a s... | [
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... | ||
corl_2023_xJ7XL5Wt8iN | xJ7XL5Wt8iN | corl | 2,023 | CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting extrinsic rewards for each transition in the offline data. As a remedy ... | Jinxin Liu;Lipeng Zu;Li He;Donglin Wang | ;;Westlake University;Westlake University | Poster | main | Offline Reinforcement Learning;Intrinsic Rewards;Learning Skills | https://openreview.net/forum?id=xJ7XL5Wt8iN | 9 | CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting ext... | [
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... | ||
corl_2023_xQx1O7WXSA | xQx1O7WXSA | corl | 2,023 | Expansive Latent Planning for Sparse Reward Offline Reinforcement Learning | Sampling-based motion planning algorithms excel at searching global solution paths in geometrically complex settings. However, classical approaches, such as RRT, are difficult to scale beyond low-dimensional search spaces and rely on privileged knowledge e.g. about collision detection and underlying state distances. In... | Robert Gieselmann;Florian T. Pokorny | KTH Royal Institute of Technology, Stockholm, Sweden; | Oral | main | model-based reinforcement learning;planning;robot manipulation | https://openreview.net/forum?id=xQx1O7WXSA | 1 | Expansive Latent Planning for Sparse Reward Offline Reinforcement Learning
Sampling-based motion planning algorithms excel at searching global solution paths in geometrically complex settings. However, classical approaches, such as RRT, are difficult to scale beyond low-dimensional search spaces and rely on privileged ... | [
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corl_2023_xgrZkRHliXR | xgrZkRHliXR | corl | 2,023 | Learning to Design and Use Tools for Robotic Manipulation | When limited by their own morphologies, humans and some species of animals have the remarkable ability to use objects from the environment toward accomplishing otherwise impossible tasks. Robots might similarly unlock a range of additional capabilities through tool use. Recent techniques for jointly optimizing morpholo... | Ziang Liu;Stephen Tian;Michelle Guo;Karen Liu;Jiajun Wu | ;Stanford University;Computer Science Department, Stanford University;Computer Science Department, Stanford University;Stanford University | Poster | main | tool use;manipulation;design | https://openreview.net/forum?id=xgrZkRHliXR | 4 | Learning to Design and Use Tools for Robotic Manipulation
When limited by their own morphologies, humans and some species of animals have the remarkable ability to use objects from the environment toward accomplishing otherwise impossible tasks. Robots might similarly unlock a range of additional capabilities through t... | [
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corl_2023_yGkqN4hqrJ | yGkqN4hqrJ | corl | 2,023 | Fine-Tuning Generative Models as an Inference Method for Robotic Tasks | Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly af... | Orr Krupnik;Elisei Shafer;Tom Jurgenson;Aviv Tamar | Technion - Israel Institute of Technology, Technion - Israel Institute of Technology;;Technion;Technion, Technion | Poster | main | robotic learning;fine-tuning;generative models | https://github.com/orrkrup/mace/ | https://openreview.net/forum?id=yGkqN4hqrJ | 3 | Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we b... | [
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corl_2023_yHlUVHWnBN | yHlUVHWnBN | corl | 2,023 | SCONE: A Food Scooping Robot Learning Framework with Active Perception | Effectively scooping food items poses a substantial challenge for current robotic systems, due to the intricate states and diverse physical properties of food. To address this challenge, we believe in the importance of encoding food items into meaningful representations for effective food scooping. However, the distinc... | Yen-Ling Tai;Yu Chien Chiu;Yu-Wei Chao;Yi-Ting Chen | National Yang Ming Chiao Tung University;National Yang Ming Chiao Tung University;NVIDIA;National Yang Ming Chiao Tung University | Poster | main | Food Manipulation;Robot Scooping;Active Perception | https://openreview.net/forum?id=yHlUVHWnBN | 12 | SCONE: A Food Scooping Robot Learning Framework with Active Perception
Effectively scooping food items poses a substantial challenge for current robotic systems, due to the intricate states and diverse physical properties of food. To address this challenge, we believe in the importance of encoding food items into meani... | [
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corl_2023_ycy47ZX0Oc | ycy47ZX0Oc | corl | 2,023 | Leveraging 3D Reconstruction for Mechanical Search on Cluttered Shelves | Finding and grasping a target object on a cluttered shelf, especially when the target is occluded by other unknown objects and initially invisible, remains a significant challenge in robotic manipulation. While there have been advances in finding the target object by rearranging surrounding objects using specialized to... | Seungyeon Kim;Young Hun Kim;Yonghyeon Lee;Frank C. Park | Seoul National University;Seoul National University;Seoul National University;Seoul National University | Poster | main | Mechanical search;Object rearrangement;Prehensile and Non-prehensile manipulation | https://github.com/seungyeon-k/Search-for-Grasp-public | https://openreview.net/forum?id=ycy47ZX0Oc | 4 | Leveraging 3D Reconstruction for Mechanical Search on Cluttered Shelves
Finding and grasping a target object on a cluttered shelf, especially when the target is occluded by other unknown objects and initially invisible, remains a significant challenge in robotic manipulation. While there have been advances in finding t... | [
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corl_2023_yobahDU4HPP | yobahDU4HPP | corl | 2,023 | Learning Realistic Traffic Agents in Closed-loop | Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to learn human-like traffic agents directly from real-world observations collected offline, but without explicit specification of traffic ... | Chris Zhang;James Tu;Lunjun Zhang;Kelvin Wong;Simon Suo;Raquel Urtasun | Department of Computer Science, University of Toronto;Waabi Innovation;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto | Poster | main | Traffic simulation;Imitation learning;Reinforcement learning | https://openreview.net/forum?id=yobahDU4HPP | 19 | Learning Realistic Traffic Agents in Closed-loop
Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to learn human-like traffic agents directly from real-world observations collected offline... | [
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corl_2023_z3D__-nc9y | z3D__-nc9y | corl | 2,023 | Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback | Ideally, we would place a robot in a real-world environment and leave it there improving on its own by gathering more experience autonomously. However, algorithms for autonomous robotic learning have been challenging to realize in the real world. While this has often been attributed to the challenge of sample complexit... | Max Balsells I Pamies;Marcel Torne Villasevil;Zihan Wang;Samedh Desai;Pulkit Agrawal;Abhishek Gupta | Universidad Politécnica de Cataluna;Harvard University, Harvard University;University of Washington;University of Washington;Massachusetts Institute of Technology;University of Washington | Poster | main | reset-free reinforcement learning;learning from human feedback | https://github.com/guided-exploration-autonomous-rl/gear-code/tree/main | https://openreview.net/forum?id=z3D__-nc9y | 6 | Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
Ideally, we would place a robot in a real-world environment and leave it there improving on its own by gathering more experience autonomously. However, algorithms for autonomous robotic learning have been challenging to realize in the real world... | [
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corl_2023_zUiH8UUYDo | zUiH8UUYDo | corl | 2,023 | Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing | Autonomous racing presents a challenging environment for testing the limits of autonomous vehicle technology. Accurately modeling the vehicle dynamics (with all forces and tires) is critical for high-speed racing, but it remains a difficult task and requires an intricate balance between run-time computational demands a... | Jingyun Ning;Madhur Behl | University of Virginia, Charlottesville;University of Virginia | Poster | main | Gaussian Process;Vehicle Dynamics;Autonomous Vehicle;Deep Kernel Learning | https://openreview.net/forum?id=zUiH8UUYDo | 6 | Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing
Autonomous racing presents a challenging environment for testing the limits of autonomous vehicle technology. Accurately modeling the vehicle dynamics (with all forces and tires) is critical for high-speed racing, but it remains a difficult... | [
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corl_2023_zvl2LuLTtgr | zvl2LuLTtgr | corl | 2,023 | What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery | Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynamics}, yielding inaccuracies that manifest as the dynamical simulation-to-reality ... | Peide Huang;Xilun Zhang;Ziang Cao;Shiqi Liu;Mengdi Xu;Wenhao Ding;Jonathan Francis;Bingqing Chen;Ding Zhao | Carnegie Mellon University;Carnegie Mellon University;;;Carnegie Mellon University;Carnegie Mellon University;;Bosch;Carnegie Mellon University | Poster | main | sim-to-real gap;reinforcement learning;causal discovery | https://openreview.net/forum?id=zvl2LuLTtgr | 33 | What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynam... | [
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corl_2024_0M7JiV1GFN | 0M7JiV1GFN | corl | 2,024 | Provably Safe Online Multi-Agent Navigation in Unknown Environments | Control Barrier Functions (CBFs) provide safety guarantees for multi-agent navigation. However, traditional approaches require full knowledge of the environment (e.g., obstacle positions and shapes) to formulate CBFs and hence, are not applicable in unknown environments. This paper overcomes this issue by proposing an ... | Zhan Gao;Guang Yang;Jasmine Bayrooti;Amanda Prorok | ;University of Cambridge;University of Cambridge; | Poster | main | Decentralized Multi-Agent Navigation;Unknown Environment;Support Vector Machine;Graph Attention Learning;Control Barrier Function | https://openreview.net/forum?id=0M7JiV1GFN | 1 | Provably Safe Online Multi-Agent Navigation in Unknown Environments
Control Barrier Functions (CBFs) provide safety guarantees for multi-agent navigation. However, traditional approaches require full knowledge of the environment (e.g., obstacle positions and shapes) to formulate CBFs and hence, are not applicable in un... | [
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... |
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