外观
论文arXiv 2609.3861729 天前
腿式机器人的密集时间运动重定向
原文链接：Dense Temporal Motion Retargeting for Legged Robots（arXiv 2609.38617 · 发布日期：2026-09-29）
Dense Temporal Motion Retargeting for Legged Robots
针对形态差异下的动态动作重定向，DTMR联合优化每步时序与控制，并在四种人形机器人和真机迁移中验证。
核心要点
- 人类和动物动作可用于学习全身技能，但源对象与机器人形态差异要求按动力学特性调整动作。
- DTMR在单一优化中联合优化时序与控制，并在每个控制步密集调整时序。
- 该方法用GPU并行的采样式MPC求解，可只变形需要时序变化的动作片段。
- 在两小时人类动作和四种人形机器人上优于基线，动态动作尤明显，并较同预算时序优化基线更精确且约快19倍。
验证范围与局限：验证范围为两小时人类动作、四种人形机器人，并展示策略迁移到真实人形机器人。
英文摘要 (Abstract)
Legged robots can learn expressive whole-body skills from the motions of humans and animals. Due to the morphology gap between the source and the robot, however, the motion must be tailored to the dynamic properties of the robot. In particular, dynamic motions such as a jump require careful adjustment, since their timing and control are interdependent. We propose dense temporal motion retargeting (DTMR), which jointly optimizes timing and control within a single optimization, where dense means that the timing is adjusted for every control step. This dense formulation enables DTMR to deform only the parts of the motion that need a change in timing. The problem is solved with sampling-based model predictive control (MPC) in parallel on a GPU. We evaluate DTMR against baselines on two hours of human motion with four humanoid robots, where the results show that DTMR outperforms baseline methods, particularly on dynamic motions. We also show that allowing more temporal deformation yields more precise retargeting. We further compare DTMR with a baseline that optimizes the temporal dimension, where the result shows that DTMR retargets more precisely under the same deformation budget while being ~19x faster. Lastly, policies trained on our references transfer to a real humanoid robot.
作者：Jaeryeong Kim, Taerim Yoon, Jin Cheng, Sungjoon Choi, Stelian Coros
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