{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6PBFXOOHOGSBNDJIHGXMREQ75C","short_pith_number":"pith:6PBFXOOH","schema_version":"1.0","canonical_sha256":"f3c25bb9c771a4168d2839aec8921fe8ba9b03b7dea827f7046afe008fc6c1be","source":{"kind":"arxiv","id":"1707.05615","version":4},"attestation_state":"computed","paper":{"title":"Pick and Place Without Geometric Object Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andreas ten Pas, Marcus Gualtieri, Robert Platt","submitted_at":"2017-07-18T13:55:48Z","abstract_excerpt":"We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception availab"},"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":"1707.05615","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-07-18T13:55:48Z","cross_cats_sorted":[],"title_canon_sha256":"d792b065990de2d3b08e6784a2f0242ea049ee0e64c47573ce818d100f364d01","abstract_canon_sha256":"620932ca15adad446e75b3a779b0f7781fa086cc658202fa53516d1e460deefc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:44.635113Z","signature_b64":"+oeqWAzsr60C+jFhFOj2vNuA1QV8rXksbJhsmWllhqhV8AO/CCjx8zVpQI/yi60cXD3582febV5t1zZ7W8FfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3c25bb9c771a4168d2839aec8921fe8ba9b03b7dea827f7046afe008fc6c1be","last_reissued_at":"2026-05-18T00:22:44.634655Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:44.634655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pick and Place Without Geometric Object Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andreas ten Pas, Marcus Gualtieri, Robert Platt","submitted_at":"2017-07-18T13:55:48Z","abstract_excerpt":"We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception availab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05615","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1707.05615","created_at":"2026-05-18T00:22:44.634727+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.05615v4","created_at":"2026-05-18T00:22:44.634727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05615","created_at":"2026-05-18T00:22:44.634727+00:00"},{"alias_kind":"pith_short_12","alias_value":"6PBFXOOHOGSB","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6PBFXOOHOGSBNDJI","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6PBFXOOH","created_at":"2026-05-18T12:31:03.183658+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/6PBFXOOHOGSBNDJIHGXMREQ75C","json":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C.json","graph_json":"https://pith.science/api/pith-number/6PBFXOOHOGSBNDJIHGXMREQ75C/graph.json","events_json":"https://pith.science/api/pith-number/6PBFXOOHOGSBNDJIHGXMREQ75C/events.json","paper":"https://pith.science/paper/6PBFXOOH"},"agent_actions":{"view_html":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C","download_json":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C.json","view_paper":"https://pith.science/paper/6PBFXOOH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.05615&json=true","fetch_graph":"https://pith.science/api/pith-number/6PBFXOOHOGSBNDJIHGXMREQ75C/graph.json","fetch_events":"https://pith.science/api/pith-number/6PBFXOOHOGSBNDJIHGXMREQ75C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C/action/storage_attestation","attest_author":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C/action/author_attestation","sign_citation":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C/action/citation_signature","submit_replication":"https://pith.science/pith/6PBFXOOHOGSBNDJIHGXMREQ75C/action/replication_record"}},"created_at":"2026-05-18T00:22:44.634727+00:00","updated_at":"2026-05-18T00:22:44.634727+00:00"}