Pre-Grasp Sliding Manipulation of Thin Objects Using Soft, Compliant, or Underactuated Hands

Abstract

We address the problem of pregrasp sliding manipulation, which is an essential skill when a thin object cannot be directly grasped from a flat surface. Leveraged on the passive reconfigurability of soft, compliant, or underactuated robotic hands, we formulate this problem as an integrated motion and grasp planning problem, and plan the manipulation directly in the robot configuration space. Rather than explicitly precomputing a pair of valid start and goal configurations, and then in a separate step planning a path to connect them, our planner actively samples start and goal robot configurations from configuration sampleable regions modeled from the geometries of the object and support surface. While randomly connecting the sampled start and goal configurations in pairs, the planner verifies whether any connected pair can achieve the task to finally confirm a solution. The proposed planner is implemented and evaluated both in simulation and on a real robot. Given the inherent compliance of the employed Yale T42 hand, we relax the motion constraints and show that the planning performance is significantly boosted. Moreover, we show that our planner outperforms two baseline planners, and that it can deal with objects and support surfaces of arbitrary geometries and sizes.