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

arXiv: https://arxiv.org/abs/2608.03116 · HTML: https://mlsanity.com/paper/2608.03116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 30 of 30)

- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking](https://mlsanity.com/paper/2510.02252/) (2025)
- [OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction](https://mlsanity.com/paper/2509.26633/) (2025)
- [Visual Imitation Enables Contextual Humanoid Control](https://mlsanity.com/paper/2505.03729/) (2025)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo](https://mlsanity.com/paper/2212.00541/) (2022)
- [TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System](https://mlsanity.com/paper/2511.02832/) (2025)
- [Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation](https://mlsanity.com/paper/2409.20514/) (2024)
- [Optimization-Based Control for Dynamic Legged Robots](https://mlsanity.com/paper/2211.11644/) (2022)
- [mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2601.22074/) (2026)
- [Fast Contact-Implicit Model-Predictive Control](https://mlsanity.com/paper/2107.05616/) (2021)
- [Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation](https://mlsanity.com/paper/2103.01104/) (2021)
- [Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control](https://mlsanity.com/paper/2309.01813/) (2023)
- [SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows](https://mlsanity.com/paper/2208.08274/) (2022)
- [Highly-Efficient Differentiable Simulation for Robotics](https://mlsanity.com/paper/2409.07107/) (2024)
- [Bi-Level Motion Imitation for Humanoid Robots](https://mlsanity.com/paper/2410.01968/) (2024)
- [Whole-Body Model-Predictive Control of Legged Robots with MuJoCo](https://mlsanity.com/paper/2503.04613/) (2025)
- [DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization](https://mlsanity.com/paper/2602.06827/) (2026)
- [Make Tracking Easy: Neural Motion Retargeting for Humanoid Whole-body Control](https://mlsanity.com/paper/2603.22201/) (2026)
- [Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running](https://mlsanity.com/paper/2603.25902/) (2026)
- [OmniTrack: General Motion Tracking via Physics-Consistent Reference](https://mlsanity.com/paper/2602.23832/) (2026)
- [Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization](https://mlsanity.com/paper/2603.09956/) (2026)
- [Contact-Implicit Trajectory Optimization with Hydroelastic Contact and iLQR](https://mlsanity.com/paper/2202.13986/) (2022)
- [SPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization](https://mlsanity.com/paper/2603.11480/) (2026)
- [Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos](https://mlsanity.com/paper/2605.23762/) (2026)
- [Single-Level Differentiable Contact Simulation](https://mlsanity.com/paper/2212.06764/) (2022)
- [Irrotational Contact Fields](https://mlsanity.com/paper/2312.03908/) (2023)
- [From Compliant to Rigid Contact Simulation: a Unified and Efficient Approach](https://mlsanity.com/paper/2405.17020/) (2024)
- [Reduced-Order Model Guided Contact-Implicit Model Predictive Control for Humanoid Locomotion](https://mlsanity.com/paper/2502.15630/) (2025)

## Abstract (arXiv)

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.