Routing Manipulation of Deformable Linear Object Using Reinforcement Learning and Diffusion Policy

Tasks involving deformable linear objects (DLOs) are prevalent in daily life but pose significant challenges due to their infinite degrees of freedom and underactuated nature. Frequent contact between DLOs and surrounding objects with unknown physical parameters, such as friction, further complicates their manipulation. Performing tasks like routing ropes through a hole requires gentle yet robust manipulation, making it particularly challenging. Previous research has not adequately addressed general DLO manipulation tasks that involve intensive contact, especially in environments with rough surfaces. This paper presents a robust and delicate manipulation learning approach for the DLO routing task, leveraging reinforcement learning (RL) and diffusion policy. First, reinforcement learning agents are trained separately for rope insertion and pulling. During training, the agents are encouraged to minimize rope tension throughout task execution in environments with randomized friction to achieve delicate motion. Next, the rollouts from these agents are collected as expert demonstrations to train a diffusion policy. Our approach generates delicate motions to prevent the rope from being damaged or getting stuck on rough surfaces while remaining robust against environmental disturbances. Please refer to our project page: https://lmeee.github.io/DLOPull/