EqC：用于内省式终身机器人学习的平衡能量控制
EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning
AI总结：
提出EqC框架，将生成式动作优化转化为平衡寻求优化，结合Energy-Gated DAgger实现策略自我改进，在双臂操作任务中提升控制性能与鲁棒性，可逐步扩大机器人自主处理场景范围。
摘要：
长期机器人自主运行不仅依赖于有效的动作生成，还需要策略能识别自身行为何时不再得到充分支持，并借助辅助来改进。我们提出了Equilibrium Energy Control（EqC，平衡能量控制），这是一种基于显式条件能量景观构建的内省式终身机器人学习框架。EqC将生成式动作优化转化为寻求平衡的优化过程，而优化过程的几何结构可揭示生成的行为是否与所学景观保持一致。该能力信号支持Energy-Gated DAgger算法，其中持续的能量一致性违规会触发专家接管，产生的恢复轨迹会被聚合用于后续策略更新。EqC可自然集成到Diffusion Transformer策略和基于流匹配的视觉-语言-动作（VLA）策略中。在仿真和真实世界的双臂操作任务中，EqC的控制性能和鲁棒性与现有方法相当或更优，同时能在策略能力边界附近及时干预。真实世界研究进一步表明，这些干预可逐步扩大自主处理的场景范围，为从静态生成式控制器向自我改进的机器人系统过渡提供了可行路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
