Within the field of robotic manipulation, a central goal is to replicate the human ability to manipulate any object in any situation using a sequence of manipulation primitives such as grasping, pushing, inserting, sliding, etc. Conceptually, each manipulation primitive restricts the object and robot to move on a lower-dimensional manifold defined by the primitive's dynamic equations of motion. Likewise, a manipulation sequence represents a dynamically feasible trajectory that traverses multiple manifolds. To manipulate any object in any situation, robotic systems must include the ability to automatically synthesize manipulation primitives (manifolds) and sequence those primitives into a coherent plan (find a path across the manifolds). This paper investigates a principled approach for solving dexterous manipulation planning. This approach is based on rapidly-exploring random trees which use contact modes to guide tree expansion along primitive manifolds. This paper extends this algorithm from 2D domains to 3D domains. We validated our algorithm on a large collection of simulated 3D manipulation tasks. These tasks required our algorithm to sequence between 6-42 manipulation primitives (i.e.\! distinct contact modes). We believe this work represents an important step towards robotic manipulation capabilities which generalize across objects and environments.
翻译:在机器人操纵领域,一个中心目标是复制人类在任何情况下操纵任何物体的能力,使用一系列操纵原始程序,如抓取、推动、插入、滑动等。 理论上,每个原始操作都限制物体和机器人在原始动态运动方程式定义的较低维度上移动。 同样,一个操纵序列代表一种动态可行的轨道,可以绕过多个多维体。要在任何情况下操纵任何物体,机器人系统必须包括自动合成操纵原始(manfolds)和将这些原始程序排序成一个连贯的计划的能力(在多个元件之间找到一条路径)。本文调查了一种解决极端操纵规划的原则性方法。这一方法的基础是快速探索随机树,这些树使用接触模式引导原始元体的树木扩张。本文将这一算法从2D域扩大到3D域。我们验证了在大型模拟的3D操纵任务上的算法。这些任务需要我们的算法在6-42操纵原始程序(i.\ 明确的联系模式) 之间排序。我们认为,这项工作代表着一个重要步骤,将机器人操纵能力推向整个天体和物体的一般环境。