将传统路径规划用于有接触的机器人操作，提升稳定性和策略多样性。
Recasting Classical Motion Planning for Contact-Rich Manipulation
- 在准静态平衡流形上改进RRT算法，用势能海森矩阵定义触觉障碍与度量。
- 可自动发现多种操作策略，对应平衡流形的不同分支。
- 三类任务仅需一个势能表达式，验证方法通用性强。
本文研究如何将传统的运动规划算法重新应用于接触丰富的机器人操作任务。传统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，但在许多操作任务中，接触不可避免或对任务成功至关重要，例如创建空间或维持物理平衡。为此，我们提出触觉快速探索随机树（HapticRRT），该算法基于准静态操作中最近提出的最优性度量，利用操纵势能的（平方）海森矩阵。主要贡献包括：(i) 将经典RRT适配到准静态平衡流形上，并深化对触觉障碍和度量的理解；(ii) 发现对应于平衡流形分支的多种操作策略；(iii) 在三个不同操作任务中验证方法的通用性，每项任务仅需单一操纵势能表达式。视频见 https://youtu.be/R8aBCnCCL40。
原文摘要 · Abstract (English)
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.
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