外观
论文arXiv 2610.1879015 天前
EqC：用于内省式终身机器人学习的平衡能量控制
原文链接：EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning（arXiv 2610.18790 · 发布日期：2026-10-13）
EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning
EqC用显式条件能量景观进行动作精炼与能力判断，并在仿真和真实双臂操作中验证终身学习。
核心要点
- 长期自主不仅要生成有效动作，还需识别行为支撑不足并借助协助改进。
- EqC将生成式动作精炼表述为寻求平衡的优化，用精炼几何判断能量一致性。
- 持续能量一致性违例触发专家接管，恢复轨迹被聚合用于后续策略更新。
- 可集成到Diffusion Transformer和基于flow matching的VLA策略，在仿真和真机中匹配或提升性能与鲁棒性。
验证范围与局限：验证范围为仿真和真实世界双臂操作，以及真实世界中的自主范围扩展研究。
英文摘要 (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.
作者：Haodong Yang, Runhan Huang, Bowen Zhang, Iofai Lei, Zunzhe Zhang, Xiangcheng Zhang, Shaoting Zhu, Linzhan Mou, Yicheng Liu, Hang Zhao
来源
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