追求接触：用于动态运动重定向的接触隐式多射击方法
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
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中文总结 AI 辅助
针对现有运动重定向方法的不足，提出接触隐式多射击（DSMS）框架，可生成动力学可行的全身轨迹，加快RL训练并实现Unitree G1的零样本模拟到真实迁移。
中文摘要 AI 辅助
运动重定向方法通常优先考虑运动学相似性，而非全身动力学、接触一致性和执行器限制，导致生成的参考轨迹难以被强化学习（RL）策略复现，尤其是在接触丰富的行为场景中。本文提出一种接触隐式、基于直接模拟的多射击（DSMS）框架，该框架将运动学可行的参考转换为动力学可行的全身轨迹。通过在非线性规划中嵌入可微模拟器，DSMS可内部解决接触、摩擦、碰撞、自碰撞和关节限制问题，同时强制执行跟踪、执行器和任务约束，无需预设接触时间表或引入显式接触约束。与现有重定向方法相比，DSMS可加快运动模仿RL训练，生成的策略具有高成功率和低跟踪误差。我们进一步在Unitree G1机器人上展示了零样本模拟到真实的迁移，通过命令控制的接触丰富爬行以及高动态180度跳转变向任务实现验证。
英文摘要
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 contact-rich crawling and a highly dynamic 180-degree jump-turn.
