文脈を考慮した動作プリエントによるヒューマノイド制御の学習
Learning Context-Aware Motion Priors for Humanoid Control
既存の動作プリエントはタスクに関係なく一律に適用されるため、不適切なガイダンスを与えることがある。本論文では、タスク文脈に応じて動作プリエントを適応させるフレームワークCMPを提案し、ヒューマノイド制御タスクで性能とサンプル効率を向上させた。
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著者: Yunyang Mo, Yi Gu, Yangchen Zhou, Hanyang Cao, Renjing Xu
分類: cs.RO
原文アブストラクト
Motion priors provide powerful guidance for learning naturalistic humanoid behaviors. However, existing methods typically learn a general, task-agnostic prior from the entire reference dataset and apply it uniformly throughout policy training. As a result, the prior cannot distinguish which reference motions are relevant to the current task context, potentially providing irrelevant or conflicting guidance. We present Context-Aware Motion Priors (CMP), a framework that adapts a general motion prior to the current task context without manual skill labels, dataset partitioning, or a separate skill discovery stage. Specifically, CMP learns context-motion compatibility using high-advantage policy rollouts, while a demonstration-based objective keeps the learned relevance grounded in the reference distribution. The resulting relevance scores reweight reference supervision for training a lightweight context-conditioned adapter. To evaluate the effectiveness and generality of CMP, we instantiate it with both Adversarial Motion Priors and Score-Matching Motion Priors. Across five humanoid control tasks, CMP consistently improves task performance and sample efficiency, learns meaningful context-motion alignment, and remains robust to imbalanced reference distributions. These results show that adapting motion priors to task contexts provides more relevant guidance for humanoid policy learning.