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Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly

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Abstract

In the domain of flexible manufacturing, Deep Reinforcement Learning (DRL) has emerged as a pivotal technology for robotic assembly tasks. Despite advancements in sample efficiency and interaction safety through residual reinforcement learning with initial policies, challenges persist in achieving context generalization amidst stochastic systems characterized by large random errors and variable backgrounds. Addressing these challenges, this study introduces a novel framework that integrates task attention-based multimodal fusion with an adaptive error curriculum within a residual reinforcement learning paradigm. Our approach commences with the formulation of a task attention-based multimodal policy that synergizes task-centric visual, relative pose, and tactile data into a compact, end-to-end model. This model is explicitly designed to enhance context generalization by improving observability, thereby ensuring robustness against stochastic errors and variable backgrounds. The second facet of our framework, curriculum residual learning, introduces an adaptive error curriculum that intelligently modulates the guidance and constraints of a model-based feedback controller. This progression from perfect to significantly imperfect initial policies incrementally enhances policy robustness and learning process stability. Empirical validation demonstrates the capability of our method to efficiently acquire a high-precision policy for assembly tasks with clearances as tight as 0.1 mm and error margins up to 20 mm within a 3.5-hour training window-a feat challenging for existing RL-based methods. The results indicate a substantial reduction in average completion time by 75\(\%\) and a 34\(\%\) increase in success rate over the classical two-step approach. An ablation study was conducted to assess the contribution of each component within our framework. Real-world task experiments further corroborate the robustness and generalization of our method, achieving over a 90\(\%\) success rate in variable contexts.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This document is the results of the National Key Research and Development Program of China (Grant No. 2021YFB3301400), the research project funded by the National Natural Science Foundation of China (Grant No. 52075177 and No.52305105), Research Foundation of Guangdong Province (Grant No. 2019A050505001 and 2018KZDXM002), Guangzhou Research Foundation (Grant No. 202002030324 and 201903010028), Zhongshan Research Foundation (Grant No. 2020B2020).

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Authors and Affiliations

  1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, People’s Republic of China

    Chuang Wang, Ze Lin, Biao Liu, Chupeng Su, Gang Chen & Longhan Xie

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  1. Chuang Wang
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  2. Ze Lin
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  3. Biao Liu
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Contributions

Chuang Wang: Conceptualization, Methodology, Software, Writing - original draft. Ze Lin: Software, Investigation. Biao Liu: Visualization, Investigation. Chupeng Su: Writing - review and editing. Gang Chen: Writing - review and editing, Supervision, Funding acquisition. Longhan Xie: Supervision, Funding acquisition. All authors read and approved the final manuscript.

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Correspondence to Gang Chen or Longhan Xie.

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Wang, C., Lin, Z., Liu, B. et al. Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly. Appl Intell 54, 4713–4735 (2024). https://doi.org/10.1007/s10489-024-05417-x

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