MOSAIC: 高速残差適応による汎用人型モーション追従と遠隔操作のsim-to-realギャップの橋渡し
MOSAIC: Bridging the Sim-to-Real Gap in Generalist Humanoid Motion Tracking and Teleoperation with Rapid Residual Adaptation
多様な入力インターフェースに対応する人型ロボットの全身遠隔操作システムを構築し、インターフェース固有の残差モジュールを高速適応・蒸留することで、実機での長期的な遠隔操作を安定化させた。
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著者: Zhenguo Sun, Bo-Sheng Huang, Yibo Peng, Xukun Li, Jingyu Ma, Yu Sun, Zhe Li, Haojun Jiang, Biao Gao, Zhenshan Bing, Xinlong Wang, Alois Knoll
分類: cs.RO
原文アブストラクト
Generalist humanoid motion trackers have recently achieved strong simulation metrics by scaling data and training, yet often remain brittle on hardware during sustained teleoperation due to interface- and dynamics-induced errors. We present MOSAIC, an open-source, full-stack system for humanoid motion tracking and whole-body teleoperation across multiple interfaces. MOSAIC first learns a teleoperation-oriented general motion tracker via RL on a multi-source motion bank with adaptive resampling and rewards that emphasize world-frame motion consistency, which is critical for mobile teleoperation. To bridge the sim-to-real interface gap without sacrificing generality, MOSAIC then performs rapid residual adaptation: an interface-specific policy is trained using minimal interface-specific data, and then distilled into the general tracker through an additive residual module, outperforming naive fine-tuning or continual learning. We validate MOSAIC with systematic ablations, out-of-distribution benchmarking, and real-robot experiments demonstrating robust offline motion replay and online long-horizon teleoperation under realistic latency and noise. Project page: baai-humanoid.github.io/MOSAIC.
