Robots Learning How and Where to Approach People

Omar A Islas Ramírez (1) , Harmish Khambhaita (2) , Raja Chatila (1, 3) , Mohamed Chetouani (1) , Rachid Alami (2)

1 ISIR - Institut des Systèmes Intelligents et de Robotique
2 LAAS-RIS - Équipe Robotique et InteractionS
3 AMAC

Robot navigation in human environments has been in the eyes of researchers for the last few years. Robots operating under these circumstances have to take human awareness into consideration for safety and acceptance reasons. Nonetheless , navigation have been often treated as going towards a goal point or avoiding people, without considering the robot engaging a person or a group of people in order to interact with them. This paper presents two navigation approaches based on the use of inverse reinforcement learning (IRL) from exemplar situations. This allow us to implement two path planners that take into account social norms for navigation towards isolated people. For the first planner, we learn an appropriate way to approach a person in an open area without static obstacles, this information is used to generate robot's path plan. As for the second planner, we learn the weights of a linear combination of continuous functions that we use to generate a costmap for the approach-behavior. This costmap is then combined with others, e.g. a costmap with higher cost around obstacles, and finally a path is generated with Dijkstra's algorithm.

Mots clés: Inverse Reinforcement Learning; Approaching People; Human Aware Navigation

Domaines: Robotique [cs.RO]

Omar A Islas Ramírez, Harmish Khambhaita, Raja Chatila, Mohamed Chetouani, Rachid Alami. Robots Learning How and Where to Approach People. 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Aug 2016, New York, NY, United States. pp.347-353, ⟨10.1109/ROMAN.2016.7745154⟩. ⟨hal-01568834⟩