{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LIQSX5ZJS5HC2OHMCRLVUJ3FZK","short_pith_number":"pith:LIQSX5ZJ","schema_version":"1.0","canonical_sha256":"5a212bf729974e2d38ec14575a2765ca9ea3909c8d875788d02bf7e1be88509d","source":{"kind":"arxiv","id":"1711.02783","version":2},"attestation_state":"computed","paper":{"title":"Learning to Imagine Manipulation Goals for Robot Task Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Chris Paxton, Christian Rupprecht, Gregory D. Hager, Kapil Katyal, Raman Arora","submitted_at":"2017-11-08T01:10:01Z","abstract_excerpt":"Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics. Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world. Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment. In this work, we propose a method for learning a model encoding just such a representation for task planning. We learn"},"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":"1711.02783","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-08T01:10:01Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"e971f4b966fb27d74dc709e18d8b908489a30dd9d50fd9aec7d77fa43b5e5da2","abstract_canon_sha256":"3ba2633f3fce953146e478d9dda9ce755c0254be4769f56900c840106f1fd230"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:54.380371Z","signature_b64":"7ATmV8r1UqmTYjzqCnmWcFLY++70hfvlytxHTyrs2Gpu+oJYWyZFkv6qyBDDgUxLu5sRZCNzDl+e1JiL/2u1AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a212bf729974e2d38ec14575a2765ca9ea3909c8d875788d02bf7e1be88509d","last_reissued_at":"2026-05-18T00:30:54.379708Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:54.379708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Imagine Manipulation Goals for Robot Task Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Chris Paxton, Christian Rupprecht, Gregory D. Hager, Kapil Katyal, Raman Arora","submitted_at":"2017-11-08T01:10:01Z","abstract_excerpt":"Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics. Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world. Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment. In this work, we propose a method for learning a model encoding just such a representation for task planning. We learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02783","kind":"arxiv","version":2},"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":"1711.02783","created_at":"2026-05-18T00:30:54.379803+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.02783v2","created_at":"2026-05-18T00:30:54.379803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02783","created_at":"2026-05-18T00:30:54.379803+00:00"},{"alias_kind":"pith_short_12","alias_value":"LIQSX5ZJS5HC","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LIQSX5ZJS5HC2OHM","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LIQSX5ZJ","created_at":"2026-05-18T12:31:28.150371+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/LIQSX5ZJS5HC2OHMCRLVUJ3FZK","json":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK.json","graph_json":"https://pith.science/api/pith-number/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/graph.json","events_json":"https://pith.science/api/pith-number/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/events.json","paper":"https://pith.science/paper/LIQSX5ZJ"},"agent_actions":{"view_html":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK","download_json":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK.json","view_paper":"https://pith.science/paper/LIQSX5ZJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.02783&json=true","fetch_graph":"https://pith.science/api/pith-number/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/graph.json","fetch_events":"https://pith.science/api/pith-number/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/action/storage_attestation","attest_author":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/action/author_attestation","sign_citation":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/action/citation_signature","submit_replication":"https://pith.science/pith/LIQSX5ZJS5HC2OHMCRLVUJ3FZK/action/replication_record"}},"created_at":"2026-05-18T00:30:54.379803+00:00","updated_at":"2026-05-18T00:30:54.379803+00:00"}