* denotes equal contribution. You can also find my articles on Google Scholar.
2022
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Method for Training a Control Arrangement for a Controlled System
Alireza Ranjbar, Gerhard Neumann, Anh Vien Ngo, and Hanna Ziesche
2022
Residual feedback learning for robot control; filed May 2022, published Nov. 24, 2022
2021
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Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
Alireza Ranjbar, Ngo Anh Vien, Hanna Ziesche, Joschka Boedecker, and Gerhard Neumann
In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their limitations. To this end, residual policy learning (RPL) offers a formulation to improve existing controllers with reinforcement learning (RL) by learning an additive “residual” to the output of a given controller. However, the applicability of such an approach highly depends on the structure of the controller. Often, internal feedback signals of the controller limit an RL algorithm to adequately change the policy and, hence, learn the task. We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty.