DynDLO: Learning-Based Trajectory Planning for Dynamic Robotic Manipulation of Deformable Linear Objects

Abstract

The automatic manipulation of Deformable Linear Objects (DLOs) remains currently a challenge in robotics. Previous research on robotic DLOs manipulation has primarily addressed quasi-static DLO manipulation at low speeds, leaving the potential of dynamic DLO manipulation largely unexplored. This paper introduces DynDLO, a goal conditioned, 6-axis robot-independent Reinforcement Learning sandbox for training agents on a variety of DLO dynamic manipulation tasks. In DynDLO, a DLO attached to the robot Tool Center Point (TCP) is simulated in the MuJoCo environment. By employing a B-Spline based trajectory generation function, the agent is capable of learning single and multiple step trajectories for the TCP, which succeed in various DLO dynamic manipulation problems. Specifically, we propose tailored design strategies for the reward function according to the classification of tasks into implicit or explicit DLO shape control tasks. Experiments on four representative tasks demonstrate that DynDLO is capable of generating dynamic manipulation policies that transfer successfully from simulation to the real world, achieving high success rates without requiring real-world training.