Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly

Disassembly plays a vital role in sustainable manufacturing and recycling processes, facilitating the recovery and reuse of valuable components. However, automating disassembly, especially for flexible elements such as cables and rubber seals, poses significant challenges due to their nonlinear behavior and dynamic properties. Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. This paper presents a reinforcement learning (RL)-based control strategy for the robotic disassembly of flexible elements. The proposed method focuses on low-level control, in which the precise manipulation of the robot is essential to minimize force and avoid damage during extraction. An adaptive reward function is tailored to account for varying material properties, ensuring robust performance across different operational scenarios. The RL-based approach is evaluated in a simulation using soft actor-critic (SAC), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO) algorithms, benchmarking their effectiveness in dynamic environments. The experimental results indicate the satisfactory performance of the robot under operational conditions, achieving an adequate success rate and force minimization. Notably, there is at least a 20% reduction in force compared to traditional planning methods. The adaptive reward function further enhances the ability of the robotic system to generalize across a range of flexible element disassembly tasks, making it a promising solution for real-world applications.