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.

翻译：通用人形机器人运动跟踪器近期通过数据与训练的规模化在仿真指标上取得了显著进展，但由于接口与动力学引发的误差，在持续遥操作过程中往往在硬件上表现脆弱。我们提出了MOSAIC——一个面向多接口人形机器人运动跟踪与全身遥操作的开源全栈系统。MOSAIC首先通过强化学习，在采用自适应重采样的多源运动数据库上学习一个面向遥操作的通用运动跟踪器，其奖励机制强调世界坐标系下的运动一致性（这对移动遥操作至关重要）。为了在不牺牲通用性的前提下弥合仿真-现实接口差距，MOSAIC随后执行快速残差适应：使用极少量接口特定数据训练一个接口专用策略，然后通过一个加性残差模块将其蒸馏至通用跟踪器中，其性能优于朴素的微调或持续学习方法。我们通过系统消融实验、分布外基准测试以及真实机器人实验验证了MOSAIC，实验证明了其在现实延迟与噪声条件下具备鲁棒的离线运动复现与在线长时域遥操作能力。项目页面：baai-humanoid.github.io/MOSAIC。