Dense Temporal Motion Retargeting for Legged Robots

2026-09-29

AI Summary

This study addresses the challenge of decoupling temporal and control parameters during dynamic motion retargeting for legged robots with diverse morphologies. We propose a dense temporal motion retargeting method that introduces a novel dense temporal deformation mechanism. This mechanism jointly optimizes step-wise temporal and control parameters within a single optimization pass to precisely accommodate robot-specific dynamics, applying deformation only where necessary. Furthermore, the approach incorporates GPU-parallelized sampling-based model predictive control (MPC) to ensure computational efficiency. Experimental results demonstrate that our method achieves a 19-fold speedup over comparable baselines while yielding superior accuracy, and successfully transfers the retargeted policies to a real-world humanoid robot.

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.

Problem

• motion retargeting
• legged robots
• dynamic motions
• morphology gap
• temporal optimization

Innovation

• Dense Temporal Motion Retargeting
• Model Predictive Control
• Legged Robots
• GPU Parallelization
• Sim-to-Real Transfer