腿式机器人的密集时间运动重定向
Dense Temporal Motion Retargeting for Legged Robots
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中文总结 AI 辅助
提出密集时间运动重定向（DTMR），联合优化时机与控制，通过GPU并行MPC实现，在仿人机器人上优于基线，速度快19倍，并成功迁移至真实机器人。
中文摘要 AI 辅助
腿式机器人可以从人类和动物的运动中学习表达丰富的全身技能。然而，由于源与机器人之间的形态差异，运动必须根据机器人的动态特性进行调整。特别是跳跃等动态运动需要仔细调整，因为其时机和控制是相互依存的。我们提出了密集时间运动重定向（DTMR），它在单一优化中联合优化时机和控制，其中“密集”意味着每个控制步骤都调整时机。这种密集公式使DTMR能够仅对需要改变时机的运动部分进行变形。该问题通过基于采样的模型预测控制（MPC）在GPU上并行求解。我们在四个仿人机器人上使用两小时的人类运动数据，将DTMR与基线方法进行评估，结果表明DTMR在动态运动上尤其优于基线方法。我们还表明，允许更多的时间变形可以获得更精确的重定向。我们进一步将DTMR与一个优化时间维度的基线进行比较，结果显示在相同的变形预算下，DTMR重定向更精确，同时速度约快19倍。最后，基于我们参考训练的策略可迁移到真实仿人机器人上。
英文摘要
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.
发表机构
- ETH Zurich（苏黎世联邦理工学院）
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
