activity
20172026
most citedIntrinsically motivated reinforcement learning for human-robot interaction in the real-world

66 citations · 121 across the 13 of their papers we have counts for

collaborators
Showing 2017Show all

2 papers · 1 filter

cs.RO2017★ 30 cited

Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network

Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa +1

For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to com…

cs.RO2017

Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning

Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa +1

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…