In this paper, we consider the problem of non-prehensile manipulation using grasped objects. This problem is a superset of many common manipulation skills including instances of tool-use (e.g., grasped spatula flipping a burger) and assembly (e.g., screwdriver tightening a screw). Here, we present an algorithmic approach for non-prehensile manipulation leveraging a gripper with highly compliant and high-resolution tactile sensors. Our approach solves for robot actions that drive object poses and forces to desired values while obeying the complex dynamics induced by the sensors as well as the constraints imposed by static equilibrium, object kinematics, and frictional contact. Our method is able to produce a variety of manipulation skills and is amenable to gradient-based optimization by exploiting differentiability within contact modes (e.g., specifications of sticking or sliding contacts). We evaluate 4 variants of controllers that attempt to realize these plans and demonstrate a number of complex skills including non-prehensile planar sliding and pivoting on a variety of object geometries. The perception and controls capabilities that drive these skills are the building blocks towards dexterous and reactive autonomy in unstructured environments.

翻译：本文研究了利用已抓取物体进行非抓握式操作的问题。该问题涵盖了许多常见操作技能的超集，包括工具使用实例（例如，用已抓握的铲子翻转汉堡）和装配操作（例如，用螺丝刀拧紧螺丝）。在此，我们提出一种利用配备高柔顺性、高分辨率触觉传感器的夹持器实现非抓握式操作的算法框架。我们的方法通过求解机器人动作，在满足传感器引发的复杂动力学特性以及静力平衡、物体运动学和摩擦接触约束的条件下，驱动物体位姿与力达到期望值。该方法能够生成多种操作技能，并通过利用接触模式（例如，粘滞接触或滑动接触的设定）内的可微性，适用于基于梯度的优化。我们评估了四种试图实现这些规划的控制器变体，并在多种物体几何形态上展示了包括非抓握式平面滑动与枢轴转动在内的复杂技能。驱动这些技能的感知与控制能力，是实现在非结构化环境中灵巧且具反应性自主操作的基础构建模块。