RoboACE: Adversarial Co-Evolution for Robot Self-Improvement
Overview
This research presents RoboACE, a framework enabling robot agents to self-improve by jointly evolving curriculum and execution harnesses around frozen foundation models. The system alternates updates between proposing informative challenges based on accumulated capability evidence and responding with reusable revisions that expand execution capabilities. Starting from a compact set of LIBERO practice conditions, the resulting frozen deployment achieves 74.0% success on LIBERO-PRO, exceeding the initial deployment by 15.0 percentage points and direct execution by its VLA controller by 13.5 points. Case studies document changes to evidence-guided practice selection, drawer-skill grounding, and controller intervention.
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- arXiv Robotics — research abstractsSignalRoboACE: Adversarial Co-Evolution for Robot Self-Improvement
This research presents RoboACE, a framework enabling robot agents to self-improve by jointly evolving curriculum and execution harnesses around frozen foundation models. The system alternates updates between proposing informative challenges based on accumulated capability evidence and responding with reusable revisions that expand execution capabilities. Starting from a compact set of LIBERO practice conditions, the resulting frozen deployment achieves 74.0% success on LIBERO-PRO, exceeding the initial deployment by 15.0 percentage points and direct execution by its VLA controller by 13.5 points. Case studies document changes to evidence-guided practice selection, drawer-skill grounding, and controller intervention.
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