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arXiv · 1702.07492

Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning

Ahmed Hussain Qureshi·Yutaka Nakamura·Yuichiro Yoshikawa·Hiroshi Ishiguro
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Abstract

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.

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Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, Hiroshi Ishiguro. 2017-02-24. Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning. https://arxiv.org/abs/1702.07492

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