EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning
Overview
EqC introduces a framework for introspective lifelong robot learning using an explicit conditional energy landscape, applicable to various policy architectures, including VLA policies. It improves control performance and robustness in simulation and real-world tasks by integrating with Diffusion Transformer and VLA policies.
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- arXiv Robotics — research abstractsEqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning
EqC is a framework for introspective lifelong robot learning that enables policies to recognize when their behavior is no longer supported and use assistance to improve. It casts generative action refinement as equilibrium-seeking optimization and integrates with Diffusion Transformer and VLA policies, showing improved control performance and robustness in simulation and real-world bimanual manipulation tasks.
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