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.@en

The Association for the Advancement of Artifcial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2018 Spring Symposium Series, held March 26-28, 2018, on the campus of Stanford University. The seven symposia held were AI and Society: Ethics, Safety, and Trustworthiness in Intelligent Agents; Artifcial Intelligence for the Internet of Everything; Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for WellBeing AI; Data-Effcient Reinforcement Learning; The Design of the User Experience for Artifcial Intelligence (the UX of AI); Integrated Representation, Reasoning, Learning, and Execution for Goal-Directed Autonomy; Learning, Inference, and Control of Multiagent Systems. This report, compiled from organizers of the symposia, summarizes the research of the symposia that took place.@en