{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W45SBJHGEVEO3HVW4FKVME7THZ","short_pith_number":"pith:W45SBJHG","schema_version":"1.0","canonical_sha256":"b73b20a4e62548ed9eb6e1555613f33e531cd250b4e9310bb4a4af44f9fe97ee","source":{"kind":"arxiv","id":"2407.03122","version":1},"attestation_state":"computed","paper":{"title":"IntentionNet: Map-Lite Visual Navigation at the Kilometre Scale","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Ai, David Hsu, Joel Loo, Vinay, Wei Gao","submitted_at":"2024-07-03T14:06:14Z","abstract_excerpt":"This work explores the challenges of creating a scalable and robust robot navigation system that can traverse both indoor and outdoor environments to reach distant goals. We propose a navigation system architecture called IntentionNet that employs a monolithic neural network as the low-level planner/controller, and uses a general interface that we call intentions to steer the controller. The paper proposes two types of intentions, Local Path and Environment (LPE) and Discretised Local Move (DLM), and shows that DLM is robust to significant metric positioning and mapping errors. The paper also "},"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":"2407.03122","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-07-03T14:06:14Z","cross_cats_sorted":[],"title_canon_sha256":"58ca66c41ca8b513101af751777aa6c015e201814864f256cb73d3d410965ec4","abstract_canon_sha256":"5ddfef94ca0f1928d3fb2cb7f32d06858ccc1c0bce300eefae2dab3908acb3cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:46.670514Z","signature_b64":"KDQ0DySr+7KuQ/qy5cUYs+ai9S0S2/CcqU/SFyaQvkLJEQIL8VkolmGYUYej10VK/Yge14XIIi78FObysY4TAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b73b20a4e62548ed9eb6e1555613f33e531cd250b4e9310bb4a4af44f9fe97ee","last_reissued_at":"2026-07-05T08:39:46.670032Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:46.670032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IntentionNet: Map-Lite Visual Navigation at the Kilometre Scale","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Ai, David Hsu, Joel Loo, Vinay, Wei Gao","submitted_at":"2024-07-03T14:06:14Z","abstract_excerpt":"This work explores the challenges of creating a scalable and robust robot navigation system that can traverse both indoor and outdoor environments to reach distant goals. We propose a navigation system architecture called IntentionNet that employs a monolithic neural network as the low-level planner/controller, and uses a general interface that we call intentions to steer the controller. The paper proposes two types of intentions, Local Path and Environment (LPE) and Discretised Local Move (DLM), and shows that DLM is robust to significant metric positioning and mapping errors. The paper also "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03122","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/2407.03122/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":"2407.03122","created_at":"2026-07-05T08:39:46.670089+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.03122v1","created_at":"2026-07-05T08:39:46.670089+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03122","created_at":"2026-07-05T08:39:46.670089+00:00"},{"alias_kind":"pith_short_12","alias_value":"W45SBJHGEVEO","created_at":"2026-07-05T08:39:46.670089+00:00"},{"alias_kind":"pith_short_16","alias_value":"W45SBJHGEVEO3HVW","created_at":"2026-07-05T08:39:46.670089+00:00"},{"alias_kind":"pith_short_8","alias_value":"W45SBJHG","created_at":"2026-07-05T08:39:46.670089+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21853","citing_title":"Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ","json":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ.json","graph_json":"https://pith.science/api/pith-number/W45SBJHGEVEO3HVW4FKVME7THZ/graph.json","events_json":"https://pith.science/api/pith-number/W45SBJHGEVEO3HVW4FKVME7THZ/events.json","paper":"https://pith.science/paper/W45SBJHG"},"agent_actions":{"view_html":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ","download_json":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ.json","view_paper":"https://pith.science/paper/W45SBJHG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.03122&json=true","fetch_graph":"https://pith.science/api/pith-number/W45SBJHGEVEO3HVW4FKVME7THZ/graph.json","fetch_events":"https://pith.science/api/pith-number/W45SBJHGEVEO3HVW4FKVME7THZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ/action/storage_attestation","attest_author":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ/action/author_attestation","sign_citation":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ/action/citation_signature","submit_replication":"https://pith.science/pith/W45SBJHGEVEO3HVW4FKVME7THZ/action/replication_record"}},"created_at":"2026-07-05T08:39:46.670089+00:00","updated_at":"2026-07-05T08:39:46.670089+00:00"}