Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

• URL: http://arxiv.org/abs/2305.09793v1
• Date: Tue, 16 May 2023 20:27:02 GMT
• Title: Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
• Authors: Desong Du, Shaohang Han, Naiming Qi, Haitham Bou Ammar, Jun Wang and Wei Pan
• Abstract summary: Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. This paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data. We also proposed the Lyapunov barrier actor-critic (LBAC) to search for a controller that satisfies the data-based approximation of the safety and reachability conditions.
• Score: 9.690491406456307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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