TRACE：适用于人形机器人不同本体的高效迁移学习神经全身体控制框架
TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments
AI总结：
TRACE是一种高效跨本体适配框架，可将预训练人形控制器在不同体型与动力学的机器人间复用，以极低成本实现高成功率的运动跟踪与遥操作。
摘要：
人形机器人行为基础模型（BFMs）可实现多样的全身体控制，但将预训练控制器适配至新本体仍具挑战，原因在于运动学与动力学不匹配。本文提出TRACE，一种高效跨本体适配框架，该框架在将反馈控制专门化至目标本体的同时保留预训练运动表征。TRACE首先向输入与输出投影层注入运动学先验，并对齐标称关节动力学，以减少本体不匹配并支持稳定微调；随后在经质量筛选的紧凑运动集上，将编码器蒸馏与低秩强化学习相结合，采用两阶段课程学习，先建立稳定跟踪再优化复杂运动。我们应用TRACE将SONIC从Unitree G1迁移至Unitree H2，使用1000万帧运动数据，在单张RTX PRO 6000 GPU上训练约8小时，GPU小时成本不足从头训练SONIC的0.1%。适配后的控制器在67953个评估运动上，按高度缩放跟踪准则达到96.08%的成功率。真实机器人演示涵盖动态运动跟踪与全身体遥操作，收集的演示数据可支持下游视觉-语言-动作策略的后训练。这些结果证明了预训练人形控制器在不同体型与动力学特性的机器人间的实用复用性。
英文摘要：
Humanoid behavior foundation models (BFMs) enable diverse whole-body control, but adapting a pretrained controller to a new embodiment remains challenging because of kinematic and dynamic mismatch. We present TRACE, a framework for efficient cross-embodiment adaptation that preserves pretrained motion representations while specializing feedback control to the target robot.TRACE first injects kinematic priors into input and output projection layers and aligns nominal joint dynamics to reduce embodiment mismatch and support stable fine-tuning. It then combines encoder distillation with low-rank reinforcement learning on a compact, quality-screened motion set, using a two-phase curriculum to establish stable tracking before refining difficult motions. We apply TRACE to transfer SONIC from Unitree G1 to Unitree H2 using 10 million motion frames and approximately eight hours of training on a single RTX PRO 6000 GPU, at less than 0.1% of the GPU-hour cost of training SONIC from scratch. The adapted controller achieves 96.08\% success on 67,953 evaluation motions under a height-scaled tracking criterion. Real-robot demonstrations span dynamic motion tracking and whole-body teleoperation, with collected demonstrations supporting downstream vision-language-action policy post-training. These results demonstrate practical reuse of a pretrained humanoid controller across robots with different body scales and dynamics.
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