報酬なし連続適応による耐障害宇宙ロボット
Reward-Free Continual Adaptation for Resilient Space Robots
宇宙ロボットのハードウェア劣化に対応するため、報酬信号なしで適応可能な報酬フリー連続学習フレームワークを提案。事前学習した世界モデルの遷移ダイナミクスのみを更新し、想像上の軌道でポリシーを訓練する。
詳しい要約
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著者: Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a promising mechanism for online adaptation, it inherently requires access to a reward signal during deployment. However, precise reward computation in space is often infeasible due to the lack of external tracking systems and the overall complexity of the environment. To address the challenge of unobservable rewards, we introduce a reward-free continual learning framework that leverages latent-state world models. By pre-training a model-based agent across diverse simulations, the world model learns a robust predictor of the reward structure within its latent space. Upon deployment to an environment with severe hardware degradation, we freeze the observation encoder and reward predictor to update only the transition dynamics of the world model through unsupervised rollouts. By training the policy entirely on imagined trajectories generated by this updated world model, the agent adapts to altered dynamics without receiving new rewards. We demonstrate our approach across simulated planetary traversal, orbital navigation, and precision assembly tasks subjected to severe morphological failures.