EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning

Haodong Yang; Runhan Huang; Bowen Zhang; Iofai Lei; Zunzhe Zhang; Xiangcheng Zhang; Shaoting Zhu; Linzhan Mou; Yicheng Liu; Hang Zhao

arXiv · 2610.18790

EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning

Haodong Yang·Runhan Huang·Bowen Zhang·Iofai Lei·Zunzhe Zhang·Xiangcheng Zhang·Shaoting Zhu·Linzhan Mou·Yicheng Liu·Hang Zhao

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Abstract

Long-term robot autonomy depends on more than effective action generation: a policy must recognize when its behavior is no longer sufficiently supported and use assistance to improve. We introduce Equilibrium Energy Control (EqC), a framework for introspective lifelong robot learning built around an explicit conditional energy landscape. EqC casts generative action refinement as equilibrium-seeking optimization, while the geometry of the refinement process reveals whether the generated behavior remains consistent with the learned landscape. This competence signal enables Energy-Gated DAgger, where persistent energy-consistency violations trigger expert takeover and the resulting recovery trajectories are aggregated for subsequent policy updates. EqC integrates naturally into both Diffusion Transformer policies and flow-matching-based vision-language-action (VLA) policies. Across simulation and real-world bimanual manipulation, EqC matches or improves control performance and robustness while enabling timely intervention near the policy's competence boundary. A real-world study further shows how these interventions progressively expand the range of situations handled autonomously, providing a practical path from static generative controllers toward self-improving robot systems.

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Haodong Yang, Runhan Huang, Bowen Zhang, Iofai Lei, Zunzhe Zhang, Xiangcheng Zhang, Shaoting Zhu, Linzhan Mou, Yicheng Liu, Hang Zhao. 2026-10-13. EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning. https://arxiv.org/abs/2610.18790

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