2017/02/24 by Ahmed Hussain Qureshi, Ahmed H. Qureshi, Qureshi, Ahmed Hussain +6 · 1 voice
Computer Science · Mathematics · Psychology · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Robotics (cs.RO) #Social Robot Interaction and HRI #cs.AI #cs.CV #cs.RO #stat.ML
paper · pdf · doi:10.48550/arxiv.1702.07492
openalex publication_date 2017/02/24 · arxiv published 2017/02/24 · arxiv updated 2017/02/24 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.