Assembly robots with optimized control stiffness through reinforcement learning
This work addresses the need for precise and damage-free robotic assembly, which is incremental as it applies RL to a known bottleneck in contact-rich manipulations.
The authors tackled the problem of automating contact-rich assembly tasks in robots by using reinforcement learning to optimize control stiffness, achieving high performance in experiments with two tasks.
There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied to realize high accuracy. In this study, we propose a methodology that uses reinforcement learning (RL) to achieve high performance in robots for the execution of assembly tasks that require precise contact with objects without causing damage. The proposed method ensures the online generation of stiffness matrices that help improve the performance of local trajectory optimization. The method has an advantage of rapid response owing to short sampling time of the trajectory planning. The effectiveness of the method was verified via experiments involving two contact-rich tasks. The results indicate that the proposed method can be implemented in various contact-rich manipulations. A demonstration video shows the performance. (https://youtu.be/gxSCl7Tp4-0)