{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2LFCJQTB2I7H4XFSPXSMMCH2BE","short_pith_number":"pith:2LFCJQTB","schema_version":"1.0","canonical_sha256":"d2ca24c261d23e7e5cb27de4c608fa0916dd680028f937fc1a1c4f9bbea4ad1a","source":{"kind":"arxiv","id":"2401.14403","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Mobile Manipulation for Articulated Objects In the Open World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Deepak Pathak, Haoyu Xiong, Kenneth Shaw, Russell Mendonca","submitted_at":"2024-01-25T18:59:44Z","abstract_excerpt":"Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework "},"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":"2401.14403","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-01-25T18:59:44Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"e0b68d02e30a261aea1c77925418b90077f5b7fa16bba234ea7213507b5b70c9","abstract_canon_sha256":"8dcb2e0206e319f2e3c6ecfea72a46ca56eec2cda54319916ab155a35379f549"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:35.159377Z","signature_b64":"e5XOf0Qp1i33kjAZpjAzFoTSZseb4kzrf0/SKvrQdwNh0qrNnfiObyBFeyDl8M6ZfQYRCPP+tJHN9QJ8epwnCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2ca24c261d23e7e5cb27de4c608fa0916dd680028f937fc1a1c4f9bbea4ad1a","last_reissued_at":"2026-07-05T07:38:35.158894Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:35.158894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Mobile Manipulation for Articulated Objects In the Open World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Deepak Pathak, Haoyu Xiong, Kenneth Shaw, Russell Mendonca","submitted_at":"2024-01-25T18:59:44Z","abstract_excerpt":"Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14403","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.14403/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":"2401.14403","created_at":"2026-07-05T07:38:35.158961+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.14403v2","created_at":"2026-07-05T07:38:35.158961+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14403","created_at":"2026-07-05T07:38:35.158961+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LFCJQTB2I7H","created_at":"2026-07-05T07:38:35.158961+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LFCJQTB2I7H4XFS","created_at":"2026-07-05T07:38:35.158961+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LFCJQTB","created_at":"2026-07-05T07:38:35.158961+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26425","citing_title":"A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29940","citing_title":"WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2310.02635","citing_title":"Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2503.10070","citing_title":"AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2603.03243","citing_title":"HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15352","citing_title":"Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12509","citing_title":"Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05828","citing_title":"Precise Aggressive Aerial Maneuvers with Sensorimotor Policies","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE","json":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE.json","graph_json":"https://pith.science/api/pith-number/2LFCJQTB2I7H4XFSPXSMMCH2BE/graph.json","events_json":"https://pith.science/api/pith-number/2LFCJQTB2I7H4XFSPXSMMCH2BE/events.json","paper":"https://pith.science/paper/2LFCJQTB"},"agent_actions":{"view_html":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE","download_json":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE.json","view_paper":"https://pith.science/paper/2LFCJQTB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.14403&json=true","fetch_graph":"https://pith.science/api/pith-number/2LFCJQTB2I7H4XFSPXSMMCH2BE/graph.json","fetch_events":"https://pith.science/api/pith-number/2LFCJQTB2I7H4XFSPXSMMCH2BE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE/action/storage_attestation","attest_author":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE/action/author_attestation","sign_citation":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE/action/citation_signature","submit_replication":"https://pith.science/pith/2LFCJQTB2I7H4XFSPXSMMCH2BE/action/replication_record"}},"created_at":"2026-07-05T07:38:35.158961+00:00","updated_at":"2026-07-05T07:38:35.158961+00:00"}