Learning Stable Normalizing-Flow Control for Robotic Manipulation
Shahbaz Abdul Khader,Hang Yin,Pietro Falco,Danica Kragic,Shahbaz Abdul Khader,Hang Yin,Pietro Falco,Danica Kragic
Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does ...
