For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills through a trial and error method. This paper aims to develop a robot that gathers data during its interaction with a human and learns human interaction behaviour from the high-dimensional sensory information using end-to-end reinforcement learning. This paper demonstrates that the robot was able to learn basic interaction skills successfully, after 14 days of interacting with people.
@article{arxiv.1702.07492,
title = {Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning},
author = {Ahmed Hussain Qureshi and Yutaka Nakamura and Yuichiro Yoshikawa and Hiroshi Ishiguro},
journal= {arXiv preprint arXiv:1702.07492},
year = {2017}
}
Comments
The paper is published in IEEE-RAS International Conference on Humanoid Robots (Humanoids) 2016