Development of a Framework for Continual Learning in Industrial Robotics
Minh Phuong Trinh, Jiyoung Moon, Lukas Grundel, Victoria Hankemeier, Simon Storms, Christian Brecher · 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) · 2022
Continual learning (CL) is a machine learning (ML) paradigm for learning continually from non-stationary data streams while simultaneously transferring and protecting past knowledge. Therefore, CL avoids catastrophic forgetting, a common problem that arises when training ML-models on new data. This paper presents a CL framework for data-driven learning of the dynamics model of a 6-degree-of-freedom serial industrial robot. This model can be used for model-based control algorithms, without the need for extensive identification of robot specific parameters such as mass inertia, and can additionally model complex effects such as friction. Furthermore, using CL, it can adapt to changes of the robot e.g., due to wear or new tasks. With the help of CL, the ML-based dynamics model is continually fed new data and improves over the operating period of the robot.