接触隐式Stein投影ADMM用于发现多样化的接触丰富操作策略

中文摘要 AI 辅助

接触隐式轨迹优化将接触丰富的操作问题表述为单一约束程序；然而，该单一程序运行会收敛到众多同样有效的接触模式、抓取或推动方向中的一个局部最优解。因此，所得操作策略缺乏适应性且对初始化敏感。为促进鲁棒操作，本文研究接触隐式求解器如何发现多样化的接触丰富策略。我们的方法推导出共识交替方向乘子法（ADMM）的一种变体，并结合Stein变分推断方法，以输出一组不同的接触丰富解。我们发现，将Stein斥力应用于ADMM的分离变量（而非其原始形式），能够有效覆盖可行的接触策略集合，而不会过早停滞求解器。我们在多种接触丰富的操作任务上展示了该方法的有效性，包括推动、抓取和多机器人交接。最后，我们发现与现有求解器相比，所提出的求解器形式更简单，且能够发现独特的接触模式。包含示例的视频和代码可在 https://anon-website-submission.github.io/stein-admm-website/ 获取。

英文摘要

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/.

发表机构

• Yale University（耶鲁大学）
• University of Sydney（悉尼大学）

机构由 AI 辅助整理，请以论文原文为准。