PILOT: 非構造環境での移動操作のための知覚統合型低レベルコントローラ
PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes
人間型ロボットが複雑な非構造環境で安定して移動しながら操作を行うため、知覚情報と固有感覚を融合し、複数の運動スキルを専門家混合で調整する単一の強化学習ポリシーを提案。シミュレーションと実機で有効性を検証。
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著者: Xinru Cui, Linxi Feng, Yixuan Zhou, Haoqi Han, Zhe Liu, Hesheng Wang
分類: cs.RO
原文アブストラクト
Humanoid robots hold great potential for diverse interactions and daily service tasks within human-centered environments, necessitating controllers that seamlessly integrate precise locomotion with dexterous manipulation. However, most existing whole-body controllers lack exteroceptive awareness of the surrounding environment, rendering them insufficient for stable task execution in complex, unstructured scenarios.To address this challenge, we propose PILOT, a unified single-stage reinforcement learning (RL) framework tailored for perceptive loco-manipulation, which synergizes perceptive locomotion and expansive whole-body control within a single policy. To enhance terrain awareness and ensure precise foot placement, we design a cross-modal context encoder that fuses prediction-based proprioceptive features with attention-based perceptive representations. Furthermore, we introduce a Mixture-of-Experts (MoE) policy architecture to coordinate diverse motor skills, facilitating better specialization across distinct motion patterns. Extensive experiments in both simulation and on the physical Unitree G1 humanoid robot validate the efficacy of our framework. PILOT demonstrates superior stability, command tracking precision, and terrain traversability compared to existing baselines. These results highlight its potential to serve as a robust, foundational low-level controller for loco-manipulation in unstructured scenes.
