Title: Haptic Rapidly-Exploring Random Trees: A Sampling-based Planner for Quasi-static Manipulation Tasks Show Chinese title

Title: 基于采样的准静态操作任务快速探索随机树 planners

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 manipulation tasks, intentional contact is often necessary. For example, when dealing with a crowded bookshelf, a robot must strategically push books aside before inserting a new one. In such scenarios, classical motion planners often fail because of insufficient space. 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 a framework that re-frames quasi-static manipulation as a planning problem on an implicit equilibrium manifold; ii) discovering multiple manipulation strategies, corresponding to branches of the equilibrium manifold. iii) providing deeper insight to haptic obstacle and haptic metric, enhancing interpretability. We validate our approach on a simulated pendulum and a real-world crowded bookshelf task, demonstrating its ability to autonomously discover strategic wedging-in policies and multiple branches. The video can be found at https://youtu.be/D-zpI0RznZ4 Show Chinese abstract

Abstract: 在这项工作中，我们探索了传统运动规划算法如何能被重新应用于接触丰富的操作任务。 我们不仅关注效率，还研究了如何用传统的运动规划算法来重新表述操作方面的问题。 传统的运动规划器，如快速探索随机树（RRT），通常会在构型空间中计算无碰撞路径。 然而，在操作任务中，有意的接触往往是必要的。 例如，当处理拥挤的书架时，机器人必须先战略性地推开一些书，然后才能插入一本新书。 在这种情况下，经典的运动规划器常常因为空间不足而失败。 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，它在一个隐式的平衡流形上，结合了一个最近提出的最优性度量，用于\textit{准静态的}操作，基于操作潜力的（平方）Hessian矩阵。 主要贡献包括：i) 将经典RRT适应于一个框架，该框架将准静态操作重新表述为隐式平衡流形上的规划问题；ii) 发现多种操作策略，对应于平衡流形的分支；iii) 对触觉障碍和触觉度量提供更深入的见解，增强可解释性。 我们在模拟摆锤和真实世界的拥挤书架任务中验证了我们的方法，展示了其自主发现战略性楔入策略和多个分支的能力。 视频可以在 https://youtu.be/D-zpI0RznZ4 找到。