Evaluating a Movable Palm in Caging Inspired Grasping using a Reinforcement Learning-based Approach

Luke Beddow, Helge Wurdemann, Dimitrios Kanoulas

Abstract: In this paper, we study the effectiveness of using a rigid movable palm for grasping varied objects, on a caging inspired gripper with three flexible fingers. This rigid palm extends to actively exert downwards force on objects, in contrast with existing methods, which combine movable palms with negative pressure to exert lifting forces on objects. We compare grasping with and without the palm, whilst also changing finger stiffness and fingertip angle, to analyse the effect on grasp success rate and stability over 24 design permutations. Reinforcement learning was used to train a unique grasping controller in every design case, aiming to achieve optimal grasping as the basis for comparison. Validation in both simulation and the real world was completed for every permutation. We demonstrated that the using palm improved success rates on average by 11% in simulation, 13% in the real world, and achieved a best real world success rate of 96% on 18 YCB benchmark food objects. Grasp stability against disturbances in three axes improved by 15% on average when using the palm. Our investigation determined fingertip angle had a large effect, whereas finger stiffness was less important.