{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YJ3DCNT2J4RQMWZLITCASCJMJP","short_pith_number":"pith:YJ3DCNT2","schema_version":"1.0","canonical_sha256":"c27631367a4f23065b2b44c409092c4beaa6ae8bd48215497cf17719e5625af1","source":{"kind":"arxiv","id":"2303.01484","version":1},"attestation_state":"computed","paper":{"title":"Predicting Motion Plans for Articulating Everyday Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Arjun Gupta, Max E. Shepherd, Saurabh Gupta","submitted_at":"2023-03-02T18:45:02Z","abstract_excerpt":"Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that "},"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":"2303.01484","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-03-02T18:45:02Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"0d9b24d6fe35b91e2eb30000b92a4a6dfaa88b80d629f5672a66d63f91e5e6ac","abstract_canon_sha256":"bca34d5e66db6e7ccf8eccff719e490db58598985c1d12282c2e1a99b899fed6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:31.520248Z","signature_b64":"rJGNS1RUL4XaOhAzj5i4NAci5r6CQ8CItJVwISO46H6o4J/hmh7iqrkK1F3N7EYP+IwciOZ1zAEB9wuP26yoCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c27631367a4f23065b2b44c409092c4beaa6ae8bd48215497cf17719e5625af1","last_reissued_at":"2026-07-05T05:47:31.519841Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:31.519841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Motion Plans for Articulating Everyday Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Arjun Gupta, Max E. Shepherd, Saurabh Gupta","submitted_at":"2023-03-02T18:45:02Z","abstract_excerpt":"Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.01484","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/2303.01484/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":"2303.01484","created_at":"2026-07-05T05:47:31.519901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.01484v1","created_at":"2026-07-05T05:47:31.519901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.01484","created_at":"2026-07-05T05:47:31.519901+00:00"},{"alias_kind":"pith_short_12","alias_value":"YJ3DCNT2J4RQ","created_at":"2026-07-05T05:47:31.519901+00:00"},{"alias_kind":"pith_short_16","alias_value":"YJ3DCNT2J4RQMWZL","created_at":"2026-07-05T05:47:31.519901+00:00"},{"alias_kind":"pith_short_8","alias_value":"YJ3DCNT2","created_at":"2026-07-05T05:47:31.519901+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/YJ3DCNT2J4RQMWZLITCASCJMJP","json":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP.json","graph_json":"https://pith.science/api/pith-number/YJ3DCNT2J4RQMWZLITCASCJMJP/graph.json","events_json":"https://pith.science/api/pith-number/YJ3DCNT2J4RQMWZLITCASCJMJP/events.json","paper":"https://pith.science/paper/YJ3DCNT2"},"agent_actions":{"view_html":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP","download_json":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP.json","view_paper":"https://pith.science/paper/YJ3DCNT2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.01484&json=true","fetch_graph":"https://pith.science/api/pith-number/YJ3DCNT2J4RQMWZLITCASCJMJP/graph.json","fetch_events":"https://pith.science/api/pith-number/YJ3DCNT2J4RQMWZLITCASCJMJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP/action/storage_attestation","attest_author":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP/action/author_attestation","sign_citation":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP/action/citation_signature","submit_replication":"https://pith.science/pith/YJ3DCNT2J4RQMWZLITCASCJMJP/action/replication_record"}},"created_at":"2026-07-05T05:47:31.519901+00:00","updated_at":"2026-07-05T05:47:31.519901+00:00"}