Learning a guidance policy to navigate among dynamic agents in constrained environments with continual reinforcement learning

Mobile robots that operate in human environments require the ability to safely navigate among humans and other obstacles. Existing approaches use Deep Reinforcement Learning (DRL) to obtain safe robot behavior in such environments, but do not ensure collision avoidance or trajectory feasibility. This issue is solved by methods combining DRL with...