An AI-powered Hierarchical Communication Framework for Robust Human-Robot Collaboration in Industrial Settings
Author:
Affiliation:
1. The University of British Columbia,School of Engineering,Kelowna,B.C,Canada,V1V 1V7
2. University of Victoria,Faculty of Engineering and Computer Science,Victoria,B.C,Canada,V8P 5C2
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9900506/9900507/09900601.pdf?arnumber=9900601
Reference19 articles.
1. Natural multimodal communication for human–robot collaboration
2. A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation
3. Robust Joint Audio–Video Talker Localization in Video Conferencing Using Reliability Information—II: Bayesian Network Fusion
4. Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory
5. Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
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1. Elevating Performance for Enhancing AI-Powered Humanoid Robots Through Innovation;Advances in Computational Intelligence and Robotics;2024-06-04
2. Personalization of industrial human–robot communication through domain adaptation based on user feedback;User Modeling and User-Adapted Interaction;2024-03-22
3. Robotic pick-and-handover maneuvers with camera-based intelligent object detection and impedance control;Transactions of the Canadian Society for Mechanical Engineering;2023-12-01
4. A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory;Frontiers in Robotics and AI;2022-07-11
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