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 (SSG), 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 nonprehensile 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 SSG 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 nonprehensile motions during the grasp action.
