Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
Researchers have developed a reinforcement learning agent to design optimal excitation signals for identifying parameters in mechatronic systems. This approach automates the process, which traditionally requires expert knowledge and manual signal design to ensure hardware safety. The RL agent successfully learned to generate these signals, outperforming classical methods and demonstrating a low rate of safety violations across multiple training runs. AI
IMPACT Automates complex system identification tasks, potentially improving efficiency and safety in robotics and mechatronics.