Contact-Implicit Stein Projected ADMM for Discovery of Diverse Contact-Rich Manipulation Strategies

2026-09-23

AI Summary

本文针对接触丰富操作策略的多样性问题，提出了一种结合Stein变分推理的ADMM方法，以发现多种有效的接触策略。

Abstract

Contact-implicit trajectory optimization formulates contact-rich manipulation as a single constrained program; however, that single program run collapses onto one local optimum out of many equally valid contact modes, grasps, or push directions. As a consequence, the resulting manipulation strategy is reluctant to change and sensitive to initialization. In order to promote robust manipulation, this paper investigates how contact-implicit solvers can discover diverse contact-rich strategies. Our approach derives a variation of Consensus Alternating Direction Method of Multipliers (ADMM) combined with Stein variational inference methods to output a set of distinct contact-rich solutions. We find that applying the Stein repulsive force to ADMM's split variable (rather than its primal form) allows for effective coverage over the set of feasible contact strategies without prematurely stalling the solver. We demonstrate the effectiveness of our approach on a variety of contact-rich manipulation tasks, including pushing, grasping, and multi-robot handover. Last, we find the proposed solver is simpler in form and capable of discovering unique contact modes when compared with existing solvers. Videos and code with examples are found in https://anon-website-submission.github.io/stein-admm-website/.

Problem

• contact-implicit
• trajectory optimization
• manipulation strategy
• diverse solutions
• local optimum

Innovation

• Consensus ADMM
• Stein variational inference
• Contact-implicit optimization
• Diverse contact-rich strategies