Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
arXiv:1810.06979
The paper introduces a deep reinforcement learning framework that trains a robot to approach small groups of people in a socially appropriate way using simulated learning stages, and validates the behavior on a real robot through objective metrics and user studies.
Abstract
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-the-art model.
accepted for The 28th IEEE International Conference on Robot & Human Interactive Communication (Ro-Man)