Unsupervised Adversarial Domain Adaptation for Sim-to-Real Transfer of Tactile Images
Author:
Affiliation:
1. School of Automation, Southeast University, Nanjing, China
2. Department of Computer Science, University of Liverpool, Liverpool, U.K
3. Department of Engineering, King's College London, London, U.K
Funder
Jiangsu Province Natural Science Foundation
Zhejiang Laboratory
National Natural Science Foundation of China
EPSRC ViTac Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10106009.pdf?arnumber=10106009
Reference59 articles.
1. Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control
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5. Integrating contrastive learning with dynamic models for reinforcement learning from images
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1. Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks;IEEE Robotics and Automation Letters;2024-10
2. SingleS2R: Single sample driven Sim-to-Real transfer for Multi-Source Visual-Tactile Information Understanding using multi-scale vision transformers;Information Fusion;2024-08
3. Confidence-guided Source Label Refinement and Class-aware Pseudo-Label Thresholding for Domain-Adaptive Semantic Segmentation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
4. FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-Motor Robot Manipulation Skills;IEEE Robotics and Automation Letters;2024-06
5. Augmenting Tactile Simulators with Real-like and Zero-Shot Capabilities;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
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