Title: Recasting Classical Motion Planning for Contact-Rich Manipulation Show Chinese title

Title: 为接触丰富的操作重新构建经典运动规划

Abstract: In this work, we explore how conventional motion planning algorithms can be reapplied to contact-rich manipulation tasks. Rather than focusing solely on efficiency, we investigate how manipulation aspects can be recast in terms of conventional motion-planning algorithms. Conventional motion planners, such as Rapidly-Exploring Random Trees (RRT), typically compute collision-free paths in configuration space. However, in many manipulation tasks, contact is either unavoidable or essential for task success, such as for creating space or maintaining physical equilibrium. As such, we presents Haptic Rapidly-Exploring Random Trees (HapticRRT), a planning algorithm that incorporates a recently proposed optimality measure in the context of \textit{quasi-static} manipulation, based on the (squared) Hessian of manipulation potential. The key contributions are i) adapting classical RRT to operate on the quasi-static equilibrium manifold, while deepening the interpretation of haptic obstacles and metrics; ii) discovering multiple manipulation strategies, corresponding to branches of the equilibrium manifold. iii) validating the generality of our method across three diverse manipulation tasks, each requiring only a single manipulation potential expression. The video can be found at https://youtu.be/R8aBCnCCL40. Show Chinese abstract

Abstract: 在本工作中，我们探讨如何将传统的运动规划算法重新应用于接触丰富的操作任务。 而不是仅仅关注效率，我们研究如何将操作方面重新表述为传统运动规划算法。 传统的运动规划器，如快速探索随机树（RRT），通常在配置空间中计算无碰撞路径。 然而，在许多操作任务中，接触是不可避免的或对任务成功至关重要，例如为了创造空间或维持物理平衡。 因此，我们提出了触觉快速探索随机树（HapticRRT），这是一种规划算法，在\textit{准静态}操作的背景下结合了最近提出的一个最优性度量，基于操作势能的（平方）海森矩阵。 主要贡献包括 i) 将经典RRT适应于在准静态平衡流形上运行，同时加深对触觉障碍和度量的理解；ii) 发现多种操作策略，对应于平衡流形的分支。iii) 在三个不同的操作任务中验证了我们方法的通用性，每个任务只需一个操作势能表达式。 视频可在 https://youtu.be/R8aBCnCCL40 找到。