ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ''one-brain, many-forms'' paradigm. Progress is hindered by fragmented data, inconsistent representations, and misaligned training objectives. We present ABot-M0, a framework that builds a systematic data curation pipeline while jointly optimizing model architecture and training strategies, enabling end-to-end transformation of heterogeneous raw data into unified, efficient representations. From six public datasets, we clean, standardize, and balance samples to construct UniACT-dataset, a large-scale dataset with over 6 million trajectories and 9,500 hours of data, covering diverse robot morphologies and task scenarios. Unified pre-training improves knowledge transfer and generalization across platforms and tasks, supporting general-purpose embodied intelligence. To improve action prediction efficiency and stability, we propose the Action Manifold Hypothesis: effective robot actions lie not in the full high-dimensional space but on a low-dimensional, smooth manifold governed by physical laws and task constraints. Based on this, we introduce Action Manifold Learning (AML), which uses a DiT backbone to predict clean, continuous action sequences directly. This shifts learning from denoising to projection onto feasible manifolds, improving decoding speed and policy stability. ABot-M0 supports modular perception via a dual-stream mechanism that integrates VLM semantics with geometric priors and multi-view inputs from plug-and-play 3D modules such as VGGT and Qwen-Image-Edit, enhancing spatial understanding without modifying the backbone and mitigating standard VLM limitations in 3D reasoning. Experiments show components operate independently with additive benefits. We will release all code and pipelines for reproducibility and future research.
699290db50fb2c0be478393a
Yandan Yang
699290db50fb2c0be478393b
Shuang Zeng
699290db50fb2c0be478393c
Tong Lin
699290db50fb2c0be478393d
Xinyuan Chang
699290db50fb2c0be478393e
Dekang Qi
699290db50fb2c0be478393f
Junjin Xiao
699290db50fb2c0be4783940
Haoyun Liu
699290db50fb2c0be4783941
Ronghan Chen
699290db50fb2c0be4783942
Yuzhi Chen
699290db50fb2c0be4783943
Dongjie Huo
699290db50fb2c0be4783944
Feng Xiong
699290db50fb2c0be4783945
Xing Wei
699290db50fb2c0be4783946
Zhiheng Ma
699290db50fb2c0be4783947
Mu Xu
ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ''one-brain, many-forms'' paradigm. Progress is hindered by fragmented data, inconsistent representations, and misaligned training objectives. We present ABot-M0, a framework that builds a systematic data curation pipeline while jointly optimizing model architecture and training strategies, enabling end-to-end transformation of heterogeneous raw data into unified, efficient representations. From six public datasets, we clean, standardize, and balance samples to construct UniACT-dataset, a large-scale dataset with over 6 million trajectories and 9,500 hours of data, covering diverse robot morphologies and task scenarios. Unified pre-training improves knowledge transfer and generalization across platforms and tasks, supporting general-purpose embodied intelligence. To improve action prediction efficiency and stability, we propose the Action Manifold Hypothesis: effective robot actions lie not in the full high-dimensional space but on a low-dimensional, smooth manifold governed by physical laws and task constraints. Based on this, we introduce Action Manifold Learning (AML), which uses a DiT backbone to predict clean, continuous action sequences directly. This shifts learning from denoising to projection onto feasible manifolds, improving decoding speed and policy stability. ABot-M0 supports modular perception via a dual-stream mechanism that integrates VLM semantics with geometric priors and multi-view inputs from plug-and-play 3D modules such as VGGT and Qwen-Image-Edit, enhancing spatial understanding without modifying the backbone and mitigating standard VLM limitations in 3D reasoning. Experiments show components operate independently with additive benefits. We will release all code and pipelines for reproducibility and future research.
ABot-M0 presents a unified framework for embodied agent development that standardizes diverse robotic data and employs action manifold learning to improve prediction efficiency and stability.
embodied agents data curation pipeline model architecture training strategies unified representations pre-training knowledge transfer action manifold hypothesis action manifold learning DiT backbone perception pipeline VLM semantics geometric priors 3D modules VGGT Qwen-Image-Edit