タスク関連潜在ダイナミクスの分離による報酬モデリング
Disentangling Task-Relevant Latent Dynamics for Reward Modeling
動画からタスクに関連する潜在表現を分離し、その時間変化から密な報酬を学習する手法DiTaRを提案。ロボット操作タスクの政策学習を改善し、実世界オフラインRLにも転移可能。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Dayou Li, Jionghao Wang, Peihao Wang, Jiuzhou Lei, Hao Wang, Jichen Chai, Hezhen Hu, Minghui Zheng, Zhangyang Wang, Xin Li, Wenping Wang, Zhiwen Fan
分類: cs.RO, cs.CV
原文アブストラクト
Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited guidance provided by sparse rewards, but require substantial task-specific reward engineering. Learning rewards from videos provides a scalable, data-driven alternative to manual reward design. However, existing video-based rewards are often computed in entangled visual spaces where task-relevant state changes are confounded with extraneous visual variation. As a result, the learned reward may respond to visual distractors instead of actual task execution. To address this limitation, we propose DiTaR (Disentangling Task-Relevant Latent Dynamics for Reward Modeling), a framework for learning dense rewards from heterogeneous videos. DiTaR learns a continuous task-relevant representation from multi-frame video latents through an asymmetric two-stage procedure that suppresses task-irrelevant visual variation. In the task-relevant latent space, we train a history- and language-conditioned rectified-flow model to capture task dynamics and derive a directional dense reward from temporal changes in dynamics compatibility. Together, these components enable reward modeling directly over task-relevant dynamics rather than generic visual variation. DiTaR substantially improves policy learning on MetaWorld-v3 and ManiSkill over video-based reward baselines, and transfers to real-world offline RL through reward relabeling. Reward analyses further show that DiTaR provides more reliable rewards.
