Digital Twin-Based Transfer Learning for Collaborative Robot Systems: A Proof of Concept
International Conference on Information Networking, vol. 22, pp. 593–598
Abstract
Nowadays, the increasing trend toward digitalization has driven the extensive adoption of collaborative robotic automation across industries, yet a significant limitation is the robots' adaptability to unexpected and dynamic environments. This research introduces a Digital Twin (DT)-based Transfer Learning (TL) approach that combines DTs and Machine Learning (ML) to enhance adaptability in collaborative robot systems. The proposed system uses DT cyberspace for pre-training ML algorithms and leverages TL to apply this knowledge to real-world applications. This innovative approach efficiently trains state-of-the-art ML models, delivering exceptional performance while reducing the required time and data resources. The proof-of-concept experiments, employing the proposed DT-based TL to control soccer robots, demonstrate a remarkable 96% reduction in training time while maintaining a high level of adaptability, achieving a 70% goal accuracy rate in dynamic scenarios.
Authors 5
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Affiliation as printed
Tokyo Institute of Technology,Department of Electrical and Electronic Engineering,Tokyo,Japan
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Affiliation as printed
Tokyo Institute of Technology,Department of Electrical and Electronic Engineering,Tokyo,Japan
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
Tokyo Institute of Technology,Department of Electrical and Electronic Engineering,Tokyo,Japan
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