Robotic and AI Enabled Waste Segregation A Systematic Review of Methods Benchmarks and Challenges

Robotic and AI Enabled Waste Segregation A Systematic Review of Methods Benchmarks and Challenges | 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 Systematic Review Robotic and AI Enabled Waste Segregation A Systematic Review of Methods Benchmarks and Challenges Manish Saini, Rashmi Chawla, Amit Kulshreshtha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8474173/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 Robotic and AI-based waste sorting has advanced rapidly over the last decade, yet deployment beyond laboratory settings remains limited. This survey systematically synthesizes 192 peer-reviewed studies (2010--2025) spanning (i) machine and deep learning perception, (ii) hybrid systems integrating optimization, fuzzy logic, and IoT fusion, (iii) robotic implementations, and (iv) process-oriented approaches. Using a PRISMA-guided protocol, we detail the search strategy, screening criteria, and quality assessment, and compare methods across accuracy (Accuracy/F1, mAP), efficiency (latency, throughput, energy), and deployment readiness Technology Readiness Level(TRL), safety. The analysis reveals strong performance on small, curated datasets (e.g., YOLO-family detectors and EfficientNet classifiers), but generalization degrades under clutter, occlusion, and dataset shift; benchmarks remain small and inconsistent, and reporting often omits system-level metrics. We propose a unified taxonomy, a minimal reporting checklist, and a multimodal benchmarking agenda (RGB-D, weight, RFID) to enhance reproducibility and comparability. Finally, we outline future research directions in explainable perception, adaptive grasping, and privacy-preserving or federated learning for municipal-scale deployments. Waste sorting Robotic manipulation Object detection Sensor fusion Edge AI Federated learning 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. 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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-8474173","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":603884161,"identity":"881957eb-9d4d-4593-9bf6-b2abfc83e9b1","order_by":0,"name":"Manish Saini","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Manish","middleName":"","lastName":"Saini","suffix":""},{"id":603884162,"identity":"5fb6947b-1dc0-4395-bd20-557ecd3c2032","order_by":1,"name":"Rashmi Chawla","email":"data:image/png;base64,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","orcid":"","institution":"J.C. 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