Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints

• Main Authors: Hamaya, Masashi, Lee, Robert, Tanaka, Kazutoshi, von Drigalski, Felix, Nakashima, Chisato, Shibata, Yoshiya, Ijiri, Yoshihisa
• Format: Conference Proceeding
• Language: English
• Subjects: Aerospace electronics; Force; Learning (artificial intelligence); Robotic assembly; Soft robotics; Task analysis
• ISSN: 2577-087X
• DOI: 10.1109/ICRA40945.2020.9197327

In this study, we present a novel control framework for assembly tasks with a soft robot. Typically, existing hard robots require high frequency controllers and precise force/torque sensors for assembly tasks. The resulting robot system is complex, entailing large amounts of engineering and maintenance. Physical softness allows the robot to interact with the environment easily. We expect soft robots to perform assembly tasks without the need for high frequency force/torque controllers and sensors. However, specific data-driven approaches are needed to deal with complex models involving nonlinearity and hysteresis. If we were to apply these approaches directly, we would be required to collect very large amounts of training data. To solve this problem, we argue that by leveraging softness and environmental constraints, a robot can complete tasks in lower dimensional state and action spaces, which could greatly facilitate the exploration of appropriate assembly skills. Then, we apply a highly efficient model-based reinforcement learning method to lower dimensional systems. To verify our method, we perform a simulation for peg-in-hole tasks. The results show that our method learns the appropriate skills faster than an approach that does not consider lower dimensional systems. Moreover, we demonstrate that our method works on a real robot equipped with a compliant module on the wrist.