Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning
This addresses the challenge of programming social intelligence in robots for human coexistence, though it appears incremental as it builds on existing deep reinforcement learning methods.
The paper tackled the problem of enabling robots to learn human-like social interaction skills by proposing a Multimodal Deep Q-Network (MDQN) that uses end-to-end reinforcement learning from high-dimensional sensory data, resulting in the robot successfully learning basic interaction skills after 14 days of interaction with people.
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.