Learning Navigation Costs from Demonstration in Partially Observable Environments
Tianyu Wang,Vikas Dhiman,Nikolay Atanasov,Tianyu Wang,Vikas Dhiman,Nikolay Atanasov
This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments. The objective is to infer a cost function that explains expert-demonstrated navigation behavior while relying only on the observations and state-control trajectory used by the expert. We develop a cost function representation composed of two pa...
