Multi-Contact Motion Retargeting using Whole-body Optimization of Full Kinematics and Sequential Force Equilibrium
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Abstract (arXiv)
This paper presents a multi-contact motion adaptation framework that enables teleoperation of high degree-of-freedom (DoF) robots, such as quadrupeds and humanoids, for loco-manipulation tasks in multi-contact settings. Our proposed algorithms optimize whole-body configurations and formulate the retargeting of multi-contact motions as sequential quadratic programming, which is robust and stable near the edges of feasibility constraints. Our framework allows real-time operation of the robot and reduces cognitive load for the operator because infeasible commands are automatically adapted into physically stable and viable motions on the robot. The results in simulations with full dynamics demonstrated the effectiveness of teleoperating different legged robots interactively and generating rich multi-contact movements. We evaluated the computational efficiency of the proposed algorithms, and further validated and analyzed multi-contact loco-manipulation tasks on humanoid and quadruped robots by reaching, active pushing and various traversal on uneven terrains.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2022.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024Flow Matching Imitation Learning for Multi-Support Manipulationcited by 29
- 2025Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learningcited by 3
- 2026ReActor: Reinforcement Learning for Physics-Aware Motion Retargetingcited by 1
- 2024Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Modelscited by 0
- 2026ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting7 May 2026
- 2025Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning26 May 2025
- 2024Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models19 Jul 2024
- 2024Flow Matching Imitation Learning for Multi-Support Manipulation17 Jul 2024