most citedMeta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments

2 citations · 2 across the 1 of their papers we have counts for

collaborators

6 papers

cs.RO20212 cited

Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments

Chao Huang, Wenhao Luo, Rui Liu

As multi-robot systems (MRS) are widely used in various tasks such as natural disaster response and social security, people enthusiastically expect an MRS to be ubiquitous that a g…

cs.RO2021

Repairing Human Trust by Promptly Correcting Robot Mistakes with An Attention Transfer Model

Ruijiao Luo, Chao Huang, Yuntao Peng +2

In human-robot collaboration (HRC), human trust in the robot is the human expectation that a robot executes tasks with desired performance. A higher-level trust increases the willi…

cs.RO2020

Robot Inner Attention Modeling for Task-Adaptive Teaming of Heterogeneous Multi Robots

Chao Huang, Rui Liu

Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tas…

cs.RO2020

Trust Aware Emergency Response for A Resilient Human-Swarm Cooperative System

Yijiang Pang, Rui Liu

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a heterogeneous team, is widely used for emergent scenarios such as criminal tracking i…

cs.RO2020

Trust Repairing for Human-Swarm Cooperation inDynamic Task Response

Yijiang Pang, Rui Liu

Emergency happens in human-UAV cooperation, such as criminal activity tracking and urgent needs for ground assistance. Emergency response usually has high requirements on the motio…

cs.RO2020

Inner Attention Supported Adaptive Cooperation for Heterogeneous Multi Robots Teaming based on Multi-agent Reinforcement Learning

Chao Huang, Rui Liu

Humans can selectively focus on different information based on different tasks requirements, other people's abilities and availability. Therefore, they can adapt quickly to a compl…