Deep Reinforcement Learning of Robotic Manipulation for Whip Targeting

This paper aims at a deep reinforcement learning (DRL) controller for fast (< 1.5s) manipulation of a flexible tool (i.e., whip) to hit a target in 3D space. The controller consists of a DRL algorithm for optimizing joint motions, and a proportional-derivative (PD) mechanism for tracking the optimized motions. Their objective is to minimize the distance between the whip-end-tip and the target. The proposed controller was validated in a 7-DOF robot arm by comparing four DRL algorithms in the physical simulator MuJoCo. It shows that the proximal policy optimization (PPO) outperforms others by obtaining the maximum average reward. Notably, PPO can still effectively interact with the environment under sparse or even unrewarding conditions, making it a robust choice for complex and dynamic tasks. Our work provides preliminary knowledge of DRL applications to fast robotic arm control in flexible object manipulation.