Robot Learning
Lyapunov design for safe reinforcement learning
The Journal of Machine Learning Research
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Semi-Supervised Learning
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Online robot learning has been a goal for researchers for several decades. A problem arises when learning algorithms need to explore the environment as actions cannot easily be anticipated. Because of this, safety is a major issue when using learning algorithms. This paper presents a framework for safe robot learning by the use of region-classification and energy limitation. The main task of the framework is to ensure safety regardless of a learning algorithm's input to a system. This is necessary to allow a learning robot to explore environments without damaging itself or its surroundings. To ensure safety, the state-space is divided into fatal, supercritical, critical and safe regions, depending on the energy of the system. To show the adaptability of the framework it is used on two different systems; an actuated swinging pendulum and a mobile platform. In both cases obstacles with unknown locations must are avoided successfully.