Adaptive Load Control of Quadruped Robots Using Neural Networks

Adaptive Load Control of Quadruped Robots Using Neural Networks | 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 Adaptive Load Control of Quadruped Robots Using Neural Networks Shuai Shi, Hongfa Lei, Zheng Chen, Huiyang Cao, Bingquan Li, Yaowei Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6875150/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 In the execution of tasks, quadruped robots frequently encounter scenarios necessitating load-bearing operations. To enhance the load-bearing capacity of quadruped robots in practical tasks, this paper proposes an adaptive load framework integrated with neural networks. Initially, a neural network estimator is devised, capable of estimating the mass and position of loads placed on the robot's back through data such as the torque of the robot's joint motors and the IMU of the body. The estimation results are then transformed into six-dimensional force applied to the robot's body. By modeling the dynamics of the quadruped robot, the external forces provided by the load are incorporated into the dynamic equations of the quadruped robot, achieving the effect of adaptive load. This method has been tested in both simulation and real-world environments, validating the effectiveness of the proposed approach. Quadruped robots adaptive load control payload prediction neural network prediction Full Text Additional Declarations No competing interests reported. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6875150","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473647574,"identity":"8fea7110-737b-49bd-b773-b593f7118e2b","order_by":0,"name":"Shuai Shi","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Shi","suffix":""},{"id":473647575,"identity":"18af14cb-a2e6-428e-bdbc-d7000b66b685","order_by":1,"name":"Hongfa Lei","email":"","orcid":"","institution":"Guangdong University of 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