Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks

Abstract

In this work, we investigate how a state-of-the-art grasp planner based on deep reinforcement learning performs when applied to a soft-rigid gripper in a decluttering task. The gripper, called Soft ScoopGripper, is endowed with a rigid scoop-shaped part that facilitates the interaction with the environment and with objects. We hypothesize that the clever design of such a gripper can facilitate the learning process, reducing the number of required training steps and eliminating the need for learning non-prehensile actions, such as pushing. To validate our hypothesis, we conducted experiments in both simulated and real-world environments, comparing the selected gripper with a rigid parallel-jaw gripper and a four-fingered soft gripper. Results show that the Soft ScoopGripper learns to effectively declutter scenes using a single action (grasping) instead of two (pushing and grasping). This is due to the fact that the scoop-shaped add-on allows to perform non-prehensile motions during the grasp action.