{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EUJEJP2BS7G22C4QDQ6ESETFPZ","short_pith_number":"pith:EUJEJP2B","schema_version":"1.0","canonical_sha256":"251244bf4197cdad0b901c3c4912657e6e36c148380d9e2314c6b851172fee2a","source":{"kind":"arxiv","id":"2404.17673","version":1},"attestation_state":"computed","paper":{"title":"Learning Manipulation Tasks in Dynamic and Shared 3D Spaces","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Hariharan Arunachalam, Marc Hanheide, Sariah Mghames","submitted_at":"2024-04-26T19:40:19Z","abstract_excerpt":"Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. manipulators) in the workplace and among human operators. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal destinations, assuming the pick has been already comp"},"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":"2404.17673","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-26T19:40:19Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"8fa8a04216f19b64987f888971c0b672d696eba56723dd8e7230e62dd2760c0e","abstract_canon_sha256":"243816199738de71fb43cdb795245bcdd7e9d9c951ae9f16b3be9e442adad895"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:52.673342Z","signature_b64":"CLvh7y2QLTQBI2geEurG24Zo8wCFLDiTnTCKQ0QYi8eU/YvJXVy2zXVTzdDU9K2hEnWaxjdYn7gW9WV17nJUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"251244bf4197cdad0b901c3c4912657e6e36c148380d9e2314c6b851172fee2a","last_reissued_at":"2026-07-05T08:12:52.672854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:52.672854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Manipulation Tasks in Dynamic and Shared 3D Spaces","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Hariharan Arunachalam, Marc Hanheide, Sariah Mghames","submitted_at":"2024-04-26T19:40:19Z","abstract_excerpt":"Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. manipulators) in the workplace and among human operators. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal destinations, assuming the pick has been already comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.17673","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/2404.17673/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":"2404.17673","created_at":"2026-07-05T08:12:52.672908+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.17673v1","created_at":"2026-07-05T08:12:52.672908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.17673","created_at":"2026-07-05T08:12:52.672908+00:00"},{"alias_kind":"pith_short_12","alias_value":"EUJEJP2BS7G2","created_at":"2026-07-05T08:12:52.672908+00:00"},{"alias_kind":"pith_short_16","alias_value":"EUJEJP2BS7G22C4Q","created_at":"2026-07-05T08:12:52.672908+00:00"},{"alias_kind":"pith_short_8","alias_value":"EUJEJP2B","created_at":"2026-07-05T08:12:52.672908+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/EUJEJP2BS7G22C4QDQ6ESETFPZ","json":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ.json","graph_json":"https://pith.science/api/pith-number/EUJEJP2BS7G22C4QDQ6ESETFPZ/graph.json","events_json":"https://pith.science/api/pith-number/EUJEJP2BS7G22C4QDQ6ESETFPZ/events.json","paper":"https://pith.science/paper/EUJEJP2B"},"agent_actions":{"view_html":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ","download_json":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ.json","view_paper":"https://pith.science/paper/EUJEJP2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.17673&json=true","fetch_graph":"https://pith.science/api/pith-number/EUJEJP2BS7G22C4QDQ6ESETFPZ/graph.json","fetch_events":"https://pith.science/api/pith-number/EUJEJP2BS7G22C4QDQ6ESETFPZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ/action/storage_attestation","attest_author":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ/action/author_attestation","sign_citation":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ/action/citation_signature","submit_replication":"https://pith.science/pith/EUJEJP2BS7G22C4QDQ6ESETFPZ/action/replication_record"}},"created_at":"2026-07-05T08:12:52.672908+00:00","updated_at":"2026-07-05T08:12:52.672908+00:00"}