Du, D;
Han, S;
Qi, N;
Ammar, HB;
Wang, J;
Pan, W;
(2023)
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions.
In:
Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA).
(pp. pp. 9442-9448).
IEEE: London, UK.
Preview |
Text
2305.09793-2.pdf - Accepted Version Download (929kB) | Preview |
Abstract
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control experiments, i.e., a 2D quadrotor navigation task. The experimental findings reveal this approach's effectiveness in reachability and safety, surpassing other model-free RL methods.
Type: | Proceedings paper |
---|---|
Title: | Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions |
Event: | 2023 IEEE International Conference on Robotics and Automation (ICRA) |
Location: | London, ENGLAND |
Dates: | 29 May 2023 - 2 Jun 2023 |
ISBN-13: | 9798350323658 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICRA48891.2023.10160991 |
Publisher version: | https://doi.org/10.1109/ICRA48891.2023.10160991 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Training, Navigation, Scalability, Robot control, Stars, Reinforcement learning, Robustness |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10185764 |
Archive Staff Only
View Item |