外观
论文arXiv 2610.0789122 天前
超越重定向：基于学习式原子运动基元的低延迟鲁棒人形机器人全身遥操作
原文链接：Beyond Retargeting: Low-Latency and Robust Humanoid Whole-Body Teleoperation with Learned Atomic Motion Primitives（arXiv 2610.07891 · 发布日期：2026-10-06）
Beyond Retargeting: Low-Latency and Robust Humanoid Whole-Body Teleoperation with Learned Atomic Motion Primitives
提出无需重定向的策略与全身运动基元码本，将原始人体运动映射为关节命令，并在Unitree G1仿真和硬件上验证。
核心要点
- 现有全身遥操作依赖在线运动重定向，易引入延迟并产生物理不可行目标。
- 策略单次前向将原始人体运动直接映射到机器人关节命令，省去在线运动学适配。
- 学习全身运动基元码本，将分布外观测投影到合理原型，并从部分输入恢复全身运动。
- 在VR、光学动捕、文本生成运动和单目视频输入下，方法在延迟与鲁棒性上优于基线。
验证范围与局限：验证范围为Unitree G1的仿真和硬件实验，输入包括VR、光学动捕、文本生成运动与单目视频。
英文摘要 (Abstract)
Humanoid whole-body teleoperation translates human motion into stable robot behavior in real time. Existing systems typically rely on online motion retargeting to bridge human--robot morphological differences, but this process adds latency and can produce physically infeasible targets. Meanwhile, diverse, noisy, and partial human-motion observations often fall outside the training distribution, potentially causing unstable robot behavior. We propose a retargeting-free policy that maps raw human motion directly to robot joint commands in a single forward pass, eliminating online kinematic adaptation. To improve robustness, we learn a codebook of full-body motion primitives that projects out-of-distribution observations onto plausible motion prototypes and recovers full-body motion from partial inputs. Experiments on a Unitree~G1 in simulation and on hardware, using virtual reality, optical mocap, text-to-motion generation, and monocular video inputs, show that our method outperforms baselines in latency and robustness.
作者：Xiayan Xu, Jiyu Yu, Xingzhou Chen, Siyi Qian, Zongyu Ma, Lilu Liu, Ling Shi, Haodong Zhang
来源
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