← 研踪 PaperTrailarXiv:2608.03116 · v2
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
首次提交 2026-08-04 12:38
版本更新 2026-10-26 07:39
雷达观测 2026-10-29 00:36
摘要
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 walking, jogging, and contact-rich crawling, and a highly dynamic 180-degree jump-turn.
作者备注：Project website with additional material: https://shooting-for-contact.github.io/
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v2 · 2026-10-26 07:39 · 最新观测版本
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
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 walking, jogging, and contact-rich crawling, and a highly dynamic 180-degree jump-turn.
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