Affordance-Based Human–Robot Interaction With Reinforcement Learning
Author:
Affiliation:
1. School of Engineering, Cardiff University, Cardiff, U.K
2. Cooper Group, University of Liverpool, Liverpool, U.K
3. School of Computer Science and Informatics, Cardiff University, Cardiff, U.K
Funder
Consejo Nacional de Ciencia y Tecnología
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10082924.pdf?arnumber=10082924
Reference65 articles.
1. Human-Robot Co-Carrying Using Visual and Force Sensing
2. Improving Human-Robot Interaction Through Explainable Reinforcement Learning
3. Recent Advances in Robot Learning from Demonstration
4. Robot Fast Adaptation to Changes in Human Engagement During Simulated Dynamic Social Interaction With Active Exploration in Parameterized Reinforcement Learning
5. Development of a Mimic Robot—Learning From Demonstration Incorporating Object Detection and Multiaction Recognition
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1. Learning to bag with a simulation‐free reinforcement learning framework for robots;IET Cyber-Systems and Robotics;2024-04-11
2. A New Paradigm to Study Social and Physical Affordances as Model-Based Reinforcement Learning;2024
3. A new paradigm to study social and physical affordances as model-based reinforcement learning;Cognitive Robotics;2024
4. ADOSMNet: a novel visual affordance detection network with object shape mask guided feature encoders;Multimedia Tools and Applications;2023-09-18
5. Seg-CURL: Segmented Contrastive Unsupervised Reinforcement Learning for Sim-to-Real in Visual Robotic Manipulation;IEEE Access;2023
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