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 behavior 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.
Recently, we proposed MDQN REF for interactive behavior perception.
Robot gains social intelligence through multimodal deep reinforcement learning
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
Engineering Computer Science Mathematics