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Deep reinforcement learning has recently been widely applied in robotics tostudy tasks such as locomotion and grasping, but its application to socialhuman-robot interaction (HRI) remains a challenge. In this paper, we present adeep learning scheme that acquires a prior model of robot approaching behaviorin simulation and applies it to real-world interaction with a physical robotapproaching groups of humans. The scheme, which we refer to as Staged SocialBehavior Learning (SSBL), considers different stages of learning in socialscenarios. We learn robot approaching behaviors towards small groups insimulation and evaluate the performance of the model using objective andsubjective measures in a perceptual study and a HRI user study with humanparticipants. Results show that our model generates more socially appropriatebehavior compared to a state-of-the-art model.

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning