ADR-PNAS: a Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks

ADR-PNAS: a Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ADR-PNAS: a Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks Yu Nong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6619931/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sim-to-real transfer in robotic manipulation tasks has emerged as a crucial area of research, addressing the challenge of bridging the gap between simulated environments and real-world applications. This paper presents a comprehensive review of current methodologies and introduces novel approaches to enhance the efficacy of sim-to-real transfer. We propose a new framework, Adaptive Domain Randomization with Progressive Neural Architecture Search (ADR-PNAS), which combines adaptive domain randomization techniques with neural architecture search to optimize both the simulation parameters and the neural network architecture for improved transfer. Our experiments on a diverse set of manipulation tasks demonstrate significant improvements in transfer performance, with up to 35\% reduction in reality gap compared to state-of-the-art methods. Furthermore, we introduce a novel metric, the Transfer Efficiency Index (TEI), to quantify the effectiveness of sim-to-real transfer across different tasks and methodologies. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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