ロボットの自己改善のための敵対的共進化フレームワークRoboACE
RoboACE: Adversarial Co-Evolution for Robot Self-Improvement
固定された基盤モデルの周りでカリキュラムと実行ハーネスを交互に進化させ、ロボットエージェントが弱点を探索しながら再利用可能なスキルを自己改善する枠組みを提案。LIBERO系ベンチマークで既存手法を上回る性能を示した。
著者: Delin Zhao, Zhengrong Yue, Junlin He, Yali Wang
分類: cs.RO
原文アブストラクト
Robot agents can improve reusable skills, planning procedures, and recovery strategies from execution feedback, yet a fixed practice source leaves the search for new weaknesses unchanged. Useful practice depends on the agent's current capabilities, so the procedure that chooses practice must also be revisable. We present RoboACE (Robot Adversarial Co-Evolution), a framework that jointly evolves curriculum and execution harnesses around frozen foundation models. The curriculum harness uses accumulated capability evidence to propose informative challenges, while the execution harness responds with reusable revisions that expand execution capabilities. Alternating updates hold one side fixed as the other changes, and admitted challenges form fixed snapshots for comparing execution revisions. Starting from a compact set of LIBERO practice conditions, the resulting frozen deployment outperforms the evaluated controllers and agent systems across four LIBERO-family panels. On LIBERO-PRO, it reaches 74.0% success, 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.