{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CBGWTQNYA7IDPJRWQKZRUBB6QQ","short_pith_number":"pith:CBGWTQNY","schema_version":"1.0","canonical_sha256":"104d69c1b807d037a63682b31a043e842dc8e7d5111d60d1b961ca03ec063771","source":{"kind":"arxiv","id":"2507.03992","version":1},"attestation_state":"computed","paper":{"title":"Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Abdalla Swikir, Allen Emmanuel Binny, Hugo T. M. Kussaba, Lingyun Chen, Pushpak Jagtap, Sami Haddadin, Shreenabh Agrawal","submitted_at":"2025-07-05T10:45:01Z","abstract_excerpt":"Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dynamical system entails solving an optimization problem with bilinear matrix inequality (BMI) constraints, a non-convex problem which, depending on the number of scalar constraints and variables, demands significant computational resources and is susceptible to numerical issues such"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.03992","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-07-05T10:45:01Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"f00102f6c6ce383c3be1f1b427a427c0db1061ad3c5da0e56f4d7124118256b5","abstract_canon_sha256":"8354e8c4976d42d0312a898ac5ce23def4d0d4c6424a3ef318ac6b9a5e72c66b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:40.455426Z","signature_b64":"1HhRld/xoRUvw9XNODFJd/fFKnn1EbADv4ZPGD3JLtkRJd357J9l9ja3eppVBN2swST03Qmu66S631Tv7Xr1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"104d69c1b807d037a63682b31a043e842dc8e7d5111d60d1b961ca03ec063771","last_reissued_at":"2026-07-05T11:32:40.454995Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:40.454995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Abdalla Swikir, Allen Emmanuel Binny, Hugo T. M. Kussaba, Lingyun Chen, Pushpak Jagtap, Sami Haddadin, Shreenabh Agrawal","submitted_at":"2025-07-05T10:45:01Z","abstract_excerpt":"Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dynamical system entails solving an optimization problem with bilinear matrix inequality (BMI) constraints, a non-convex problem which, depending on the number of scalar constraints and variables, demands significant computational resources and is susceptible to numerical issues such"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.03992","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.03992/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.03992","created_at":"2026-07-05T11:32:40.455047+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.03992v1","created_at":"2026-07-05T11:32:40.455047+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.03992","created_at":"2026-07-05T11:32:40.455047+00:00"},{"alias_kind":"pith_short_12","alias_value":"CBGWTQNYA7ID","created_at":"2026-07-05T11:32:40.455047+00:00"},{"alias_kind":"pith_short_16","alias_value":"CBGWTQNYA7IDPJRW","created_at":"2026-07-05T11:32:40.455047+00:00"},{"alias_kind":"pith_short_8","alias_value":"CBGWTQNY","created_at":"2026-07-05T11:32:40.455047+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ","json":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ.json","graph_json":"https://pith.science/api/pith-number/CBGWTQNYA7IDPJRWQKZRUBB6QQ/graph.json","events_json":"https://pith.science/api/pith-number/CBGWTQNYA7IDPJRWQKZRUBB6QQ/events.json","paper":"https://pith.science/paper/CBGWTQNY"},"agent_actions":{"view_html":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ","download_json":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ.json","view_paper":"https://pith.science/paper/CBGWTQNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.03992&json=true","fetch_graph":"https://pith.science/api/pith-number/CBGWTQNYA7IDPJRWQKZRUBB6QQ/graph.json","fetch_events":"https://pith.science/api/pith-number/CBGWTQNYA7IDPJRWQKZRUBB6QQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ/action/storage_attestation","attest_author":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ/action/author_attestation","sign_citation":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ/action/citation_signature","submit_replication":"https://pith.science/pith/CBGWTQNYA7IDPJRWQKZRUBB6QQ/action/replication_record"}},"created_at":"2026-07-05T11:32:40.455047+00:00","updated_at":"2026-07-05T11:32:40.455047+00:00"}