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arXiv · 2608.03116

Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting

Sergio A. Esteban·Jason H. K. Siu·Derrick Mach·Junheng Li·Vince Kurtz·Joel W. Burdick·Aaron D. Ames
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Abstract

Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding references that are difficult for reinforcement learning (RL) policies to reproduce, particularly for contact-rich behaviors. We present a contact-implicit, direct simulation-based multiple shooting (DSMS) framework that transforms kinematically feasible references into dynamically feasible whole-body trajectories. By embedding a differentiable simulator within a nonlinear program, DSMS resolves contact, friction, impacts, self-collision, and joint limits internally while enforcing tracking, actuation, and task constraints without prescribing a contact schedule or introducing explicit contact constraints. Compared with existing retargeting methods, DSMS accelerates motion-imitation RL training and yields policies with high success rates and low tracking error. We further demonstrate zero-shot sim-to-real transfer on the Unitree G1 through command-conditioned contact-rich crawling and a highly dynamic 180-degree jump-turn.

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Sergio A. Esteban, Jason H. K. Siu, Derrick Mach, Junheng Li, Vince Kurtz, Joel W. Burdick, Aaron D. Ames. 2026-08-04. Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting. https://arxiv.org/abs/2608.03116

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