{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11999064","patent":{"patent_number":"US-11999064","title":"Excavation learning for rigid objects in clutter","assignee":null,"inventors":[],"filing_date":"2021-07-20T00:00:00.000Z","publication_date":"2024-06-04T00:00:00.000Z","cpc_codes":["B25J","B25J","B25J","B25J","G06N","G06N","G06N","G06T","G06T","G05B","G05B","G06N","G06T","G06T","G06T"],"num_claims":19,"abstract":"Embodiments of a learning-based excavation planning method are disclosed for excavating rigid objects in clutter, which is challenging due to high variance of geometric and physical properties of objects, and large resistive force during the excavation. A convolutional neural network is utilized to predict a probability of excavation success. Embodiments of a sampling-based optimization method are disclosed for planning high-quality excavation trajectories by leveraging the learned prediction model. To reduce simulation-to-real gap for excavation learning, voxel-based representations of an excavation scene are used. Excavation experiments were performed in both simulation and real world to evaluate the learning-based excavation planners. Experimental results show that embodiments of the disclosed method may plan high-quality excavations for rigid objects in clutter and outperform baseline methods by large margins."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Excavation learning for rigid objects in clutter","description":"Embodiments of a learning-based excavation planning method are disclosed for excavating rigid objects in clutter, which is challenging due to high variance of geometric and physical properties of obje","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11999064","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11999064","citation_suggestion":"Patentable. \"Excavation learning for rigid objects in clutter\" (US-11999064). https://patentable.app/patents/US-11999064","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11999064","json":"https://patentable.app/api/llm-context/US-11999064","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T14:09:57.564Z"}