EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities
Travis Davies, Yiqi Huang, Alexi Gladstone, Yunxin Liu, Xiang Chen, Heng Ji, Huxian Liu, Luhui Hu

TL;DR
EBT-Policy introduces an energy-based model for robotic control that outperforms diffusion policies in efficiency, robustness, and emergent capabilities like zero-shot recovery, demonstrating significant advances in physical reasoning and real-world applicability.
Contribution
The paper presents EBT-Policy, a scalable energy-based architecture that enhances robustness and emergent reasoning in robotic policies, outperforming existing diffusion-based methods in efficiency and capabilities.
Findings
EBT-Policy converges within two inference steps on some tasks.
It outperforms diffusion policies in simulated and real-world tasks.
Exhibits emergent zero-shot recovery capabilities without explicit retry training.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsReinforcement Learning in Robotics · Multimodal Machine Learning Applications · Robot Manipulation and Learning
