Fachbereich Informatik

Meta parameter optimization of robot force controller

Bachelor/Master Thesis at group TAMS


Force control has been validated as a powerful tool for deal with contact-rich tasks in the context of robot learning. There have developed different kinds of force control strategies in the very recent years. However, these force controllers require the mate parameters to be manually set, which is usually non-optimal. Optimization techniques could be used to refine these meta parameters, such as Covariance Matrix Adaptation Evolutionary Strategy (CMAES), Particle Swarm Optimization (PSO), and Reinforcement Learning (RL), thus to obtain optimal performances.


We have research assistant positions available. This topic could also be suitable for successful applicants. Please feel free to contact Chao Zeng if you are interested.