EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities
Implicit policies parameterized by generative models, such as Diffusion
Policy, have become the standard for policy learning and Vision-Language-Action
(VLA) models in robotics. However, these approaches often suffer from high
computational cost, exposure bias, and unstable inference dynamics, which lead
to divergence under distribution shifts. Energy-Based Models (EBMs) address
these issues by learning energy landscapes end-to-end and modeling equilibrium
dynamics, offering improved robustness and reduced exposure bias. Yet, policies
parameterized by EBMs have historically struggled to scale effectively. Recent
work on Energy-Based Transformers (EBTs) demonstrates the scalability of EBMs
to high-dimensional spaces, but their potential for solving core challenges in
physically embodied models remains underexplored. We introduce a new
energy-based architecture, EBT-Policy, that solves core issues in robotic and
real-world settings. Across simulated and real-world tasks, EBT-Policy
consistently outperforms diffusion-based policies, while requiring less
training and inference computation. Remarkably, on some tasks it converges
within just two inference steps, a 50x reduction compared to Diffusion Policy's
100. Moreover, EBT-Policy exhibits emergent capabilities not seen in prior
models, such as zero-shot recovery from failed action sequences using only
behavior cloning and without explicit retry training. By leveraging its scalar
energy for uncertainty-aware inference and dynamic compute allocation,
EBT-Policy offers a promising path toward robust, generalizable robot behavior
under distribution shifts.
690a1d7dd70e173c84528e6a
Travis Davies
claimed_verified
2025-11-06T11:48:35.243Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/fhkTu3jWHuPkZvNH4uvOV.png
Travis Davies
travisddavies
user
travisddavies
690a1d7dd70e173c84528e6b
Yiqi Huang
690a1d7dd70e173c84528e6c
Alexi Gladstone
690a1d7dd70e173c84528e6d
Yunxin Liu
690a1d7dd70e173c84528e6e
Xiang Chen
690a1d7dd70e173c84528e6f
Heng Ji
690a1d7dd70e173c84528e70
Huxian Liu
claimed_verified
2025-11-05T16:24:58.013Z
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/avatars/4be19af5efc0b676523ae79959cceb33.svg
liuhuxian
liuhuxian
user
liuhuxian
690a1d7dd70e173c84528e71
Luhui Hu
EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities
Implicit policies parameterized by generative models, such as Diffusion
Policy, have become the standard for policy learning and Vision-Language-Action
(VLA) models in robotics. However, these approaches often suffer from high
computational cost, exposure bias, and unstable inference dynamics, which lead
to divergence under distribution shifts. Energy-Based Models (EBMs) address
these issues by learning energy landscapes end-to-end and modeling equilibrium
dynamics, offering improved robustness and reduced exposure bias. Yet, policies
parameterized by EBMs have historically struggled to scale effectively. Recent
work on Energy-Based Transformers (EBTs) demonstrates the scalability of EBMs
to high-dimensional spaces, but their potential for solving core challenges in
physically embodied models remains underexplored. We introduce a new
energy-based architecture, EBT-Policy, that solves core issues in robotic and
real-world settings. Across simulated and real-world tasks, EBT-Policy
consistently outperforms diffusion-based policies, while requiring less
training and inference computation. Remarkably, on some tasks it converges
within just two inference steps, a 50x reduction compared to Diffusion Policy's
100. Moreover, EBT-Policy exhibits emergent capabilities not seen in prior
models, such as zero-shot recovery from failed action sequences using only
behavior cloning and without explicit retry training. By leveraging its scalar
energy for uncertainty-aware inference and dynamic compute allocation,
EBT-Policy offers a promising path toward robust, generalizable robot behavior
under distribution shifts.
EBT-Policy, an energy-based architecture, outperforms diffusion-based policies in robotic tasks by offering improved robustness, reduced computational cost, and emergent zero-shot recovery capabilities.
Diffusion Policy Energy-Based Models EBMs Energy-Based Transformers EBTs EBT-Policy energy landscapes equilibrium dynamics distribution shifts zero-shot recovery behavior cloning uncertainty-aware inference dynamic compute allocation