{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VOB4DTS2GJKMH3HBXTBTA6N6T4","short_pith_number":"pith:VOB4DTS2","schema_version":"1.0","canonical_sha256":"ab83c1ce5a3254c3ece1bcc33079be9f1efe7e43acd7a39563e1a0085af65406","source":{"kind":"arxiv","id":"1811.00506","version":1},"attestation_state":"computed","paper":{"title":"Navigation by Imitation in a Pedestrian-Rich Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenliang Xu, Jing Bi, Qiuyue Sun, Tianyou Xiao","submitted_at":"2018-11-01T17:20:03Z","abstract_excerpt":"Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road. However, navigation in a pedestrian-rich environment, such as a campus setup, is still challenging---one needs to take frequent interventions to the robot and take control over the robot from early steps leading to a mistake. An arduous burden is, hence, placed on the learning framework design and data acquisition. In this paper, we propose a new learning-from-intervention Dataset Aggregation (DAgger) algorithm to overcome the limitations brought by applying imitation lea"},"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":"1811.00506","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-01T17:20:03Z","cross_cats_sorted":[],"title_canon_sha256":"c234d6bc621aece7044125d0e6b6f02f89bd5f24fa236374808d83f4f6f1cbdf","abstract_canon_sha256":"837f85f60a4d5b67efcf0e0315b0dcbca02eb0bac5f85a4e1e96aef301ada02c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:44.349561Z","signature_b64":"1CK8c1e+8tdW2hLcMm+00tniuPRFn/Is4tP/XAcL5UQrR/HBt3hyY1ZYUOiUnSK9wNSpYTK5XjvKpuiplGogCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab83c1ce5a3254c3ece1bcc33079be9f1efe7e43acd7a39563e1a0085af65406","last_reissued_at":"2026-05-18T00:01:44.349065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:44.349065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Navigation by Imitation in a Pedestrian-Rich Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenliang Xu, Jing Bi, Qiuyue Sun, Tianyou Xiao","submitted_at":"2018-11-01T17:20:03Z","abstract_excerpt":"Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road. However, navigation in a pedestrian-rich environment, such as a campus setup, is still challenging---one needs to take frequent interventions to the robot and take control over the robot from early steps leading to a mistake. An arduous burden is, hence, placed on the learning framework design and data acquisition. In this paper, we propose a new learning-from-intervention Dataset Aggregation (DAgger) algorithm to overcome the limitations brought by applying imitation lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00506","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":""},"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":"1811.00506","created_at":"2026-05-18T00:01:44.349135+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00506v1","created_at":"2026-05-18T00:01:44.349135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00506","created_at":"2026-05-18T00:01:44.349135+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOB4DTS2GJKM","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOB4DTS2GJKMH3HB","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOB4DTS2","created_at":"2026-05-18T12:32:59.047623+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/VOB4DTS2GJKMH3HBXTBTA6N6T4","json":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4.json","graph_json":"https://pith.science/api/pith-number/VOB4DTS2GJKMH3HBXTBTA6N6T4/graph.json","events_json":"https://pith.science/api/pith-number/VOB4DTS2GJKMH3HBXTBTA6N6T4/events.json","paper":"https://pith.science/paper/VOB4DTS2"},"agent_actions":{"view_html":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4","download_json":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4.json","view_paper":"https://pith.science/paper/VOB4DTS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00506&json=true","fetch_graph":"https://pith.science/api/pith-number/VOB4DTS2GJKMH3HBXTBTA6N6T4/graph.json","fetch_events":"https://pith.science/api/pith-number/VOB4DTS2GJKMH3HBXTBTA6N6T4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4/action/storage_attestation","attest_author":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4/action/author_attestation","sign_citation":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4/action/citation_signature","submit_replication":"https://pith.science/pith/VOB4DTS2GJKMH3HBXTBTA6N6T4/action/replication_record"}},"created_at":"2026-05-18T00:01:44.349135+00:00","updated_at":"2026-05-18T00:01:44.349135+00:00"}