臨界性モデルを用いた身体性AIの自己進化学習
Self-Evolving Learning for Embodied AI with Criticality Model
身体性AIのファインチューニング停滞を解決するため、ポリシー自身の実行結果から将来の失敗確率を予測する臨界性モデルを学習し、失敗しやすいシナリオを重点的にサンプリングして学習データの情報密度を高める自己進化手法を提案した。
著者: Linxuan He, Yuying Tian, Lingxiang Fan, Jiaqi Pi, Yinqiao Lu, Shang Su, Mengkai Shi, Shuo Feng
分類: eess.SY
原文アブストラクト
Despite rapid advances in policy pretraining, embodied AI systems routinely plateau during task-specific finetuning. The root cause lies in how finetuning data are collected: the default pipeline gathers data randomly, treating every sample as informative. Datasets become dominated by nominal scenarios, while rare failure cases--the most valuable for improvement--are missed. We propose a self-evolving method that breaks this plateau. Our core insight is that a state-wise criticality model, learned from the policy's own execution outcomes to predict the probability of future failure, can guide importance sampling toward failure-prone scenarios. After replacing redundant nominal scenarios with diverse failure-prone ones, importance weights are used to resample the data during training. This effectively preserves an unbiased learning objective while fundamentally increasing the information density of the training pool. Across quadrupedal locomotion, multi-task manipulation, vision-language-action benchmarks, and a real-robot task, our method reduces failure rates by 51--67% relative to trained baselines and by 8-25% relative to state-of-the-art vision-language-action models.