Open-loop Deep Reinforcement Learning Control of Soft Robotic In-hand Manipulations

Abstract

In-hand manipulation tasks using hand-like robotic grippers offer a promising approach to accomplish various tasks in a human-centered environment. Due to the inherent safety of soft robots, in-hand manipulations performed by soft robots provide great opportunities for future human-robot collaboration, which is the scope of this paper. By modeling a new and innovative soft-robotic gripper known as the Anthropomorphic Soft Gripper and synthesis of an open-loop controller with deep reinforcement learning, it is demonstrated how the movement of objects by in-hand manipulations can be accomplished. Moreover, this work explores the application of deep reinforcement learning methods without the employment for domain randomization. As noted by Bhatt et al. the inherent soft properties of soft robotic grippers enable remarkably robust in-hand manipulation in open-loop control, giving the impetus for the approach that is being followed in this work. Motion sequences generated in simulation are successfully transferred to the real anthropomorphic soft gripper and validated in experiments.