让全尺寸人形机器人带重物精准远程操作
HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum
- 用特权动作引导学习真实动作，提升追踪精度
- 支持24公斤负载下完成转身、蹲起等复杂动作
- 适合需要重物操作的工业级人形机器人应用
通用运动追踪与遥操作为可扩展的人形技能获取提供了前景，但现有框架多在小型平台验证，缺乏真实负载交互。全尺寸人形机器人因惯性大、平衡余量小，对消费级VR追踪器的噪声、漂移和重定向误差极为敏感，且负载能力未被充分利用。我们提出HEFT框架，通过特权动作引导（PMG）从可部署的噪声VR参考中学习物理合理的动作重建，并采用窗口化负载课程（WPC）与专家指导的负载上限来实现稳健的重载追踪。该方法在175厘米、65公斤的L7人形机器人上部署，成功实现了在高达24公斤负载下完成转身、前后移动和蹲起等动作的精准跟踪。
原文摘要 · Abstract (English)
General motion tracking and teleoperation offer a promising path to scalable humanoid skill acquisition, yet most existing frameworks are validated on compact platforms or without real payload interaction, leaving full-size humanoids with real payloads largely unexplored. Scaling to full-size humanoids introduces two compounding challenges: their larger inertia and tighter balance margins make tracking highly sensitive to noise, drift, and retargeting errors from commodity VR trackers, while their payload potential remains largely underutilized. We present HEFT, a heavy-payload full-size humanoid teleoperation framework that addresses both challenges. HEFT learns from deployable noisy VR references with physically plausible reconstructed references through Privileged Motion Guidance (PMG), and uses a Windowed Payload Curriculum (WPC) with expert-guided payload caps to acquire robust heavy-payload tracking. We deploy HEFT on L7, a 175cm, 65kg humanoid. The robot tracks motions including turns, forward/backward locomotion, and squats under payloads up to 24kg.
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