{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BBE33A34S4N4CDQVI4CLC6FYTZ","short_pith_number":"pith:BBE33A34","canonical_record":{"source":{"id":"2503.04969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T21:02:02Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"135e1325fb0f2c522ca60f2f3626433be895317d2f56da8c85f70a2a8cd6ce86","abstract_canon_sha256":"af50092c68d5522046cfcfe41ed11231057271e3fc1e14f5fab838d8f914cf65"},"schema_version":"1.0"},"canonical_sha256":"0849bd837c971bc10e154704b178b89e49483ea0b7bda3e64b1d21ff89ff5718","source":{"kind":"arxiv","id":"2503.04969","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04969","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04969v1","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04969","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_12","alias_value":"BBE33A34S4N4","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_16","alias_value":"BBE33A34S4N4CDQV","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_8","alias_value":"BBE33A34","created_at":"2026-07-05T10:26:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BBE33A34S4N4CDQVI4CLC6FYTZ","target":"record","payload":{"canonical_record":{"source":{"id":"2503.04969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T21:02:02Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"135e1325fb0f2c522ca60f2f3626433be895317d2f56da8c85f70a2a8cd6ce86","abstract_canon_sha256":"af50092c68d5522046cfcfe41ed11231057271e3fc1e14f5fab838d8f914cf65"},"schema_version":"1.0"},"canonical_sha256":"0849bd837c971bc10e154704b178b89e49483ea0b7bda3e64b1d21ff89ff5718","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:17.603637Z","signature_b64":"sFkLOUybmKj0W4GLxnkYJh64BaULQggsBBWR2t8swVcJbQBDk4NYYZ7oilqcq/Zlam5XPUGSXGEh9hRck7j+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0849bd837c971bc10e154704b178b89e49483ea0b7bda3e64b1d21ff89ff5718","last_reissued_at":"2026-07-05T10:26:17.603168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:17.603168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.04969","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:26:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fLlQtXwJHpLer+XplWhKihRKrb4vYXP1wqaAngVKdc3c7xV5Lcm0ada42ExSNE5wwWGwJl3ldyqd+gEq0gwDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:56:50.627537Z"},"content_sha256":"e0308e590cdfcf94442b7e3a605d241c25578d8562e270bad3b863715b96dd56","schema_version":"1.0","event_id":"sha256:e0308e590cdfcf94442b7e3a605d241c25578d8562e270bad3b863715b96dd56"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BBE33A34S4N4CDQVI4CLC6FYTZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-Efficient Learning from Human Interventions for Mobile Robots","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Bolei Zhou, Zhenghao Peng, Zhizheng Liu","submitted_at":"2025-03-06T21:02:02Z","abstract_excerpt":"Mobile robots are essential in applications such as autonomous delivery and hospitality services. Applying learning-based methods to address mobile robot tasks has gained popularity due to its robustness and generalizability. Traditional methods such as Imitation Learning (IL) and Reinforcement Learning (RL) offer adaptability but require large datasets, carefully crafted reward functions, and face sim-to-real gaps, making them challenging for efficient and safe real-world deployment. We propose an online human-in-the-loop learning method PVP4Real that combines IL and RL to address these issue"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04969","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/2503.04969/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:26:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rHCd83H/AxwSTwOnTdBpCaYJK27EY7x5Mes280l/XUCT4XwD7nR3K1xfAm86S6ENWFgmakqXxnLr3LHD050JCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:56:50.628106Z"},"content_sha256":"20592c1b689582d936b5eecb0bb2bc22d1a76d4550dfa2f4717121d1b2cae174","schema_version":"1.0","event_id":"sha256:20592c1b689582d936b5eecb0bb2bc22d1a76d4550dfa2f4717121d1b2cae174"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/bundle.json","state_url":"https://pith.science/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T05:56:50Z","links":{"resolver":"https://pith.science/pith/BBE33A34S4N4CDQVI4CLC6FYTZ","bundle":"https://pith.science/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/bundle.json","state":"https://pith.science/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BBE33A34S4N4CDQVI4CLC6FYTZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BBE33A34S4N4CDQVI4CLC6FYTZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"af50092c68d5522046cfcfe41ed11231057271e3fc1e14f5fab838d8f914cf65","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T21:02:02Z","title_canon_sha256":"135e1325fb0f2c522ca60f2f3626433be895317d2f56da8c85f70a2a8cd6ce86"},"schema_version":"1.0","source":{"id":"2503.04969","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04969","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04969v1","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04969","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_12","alias_value":"BBE33A34S4N4","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_16","alias_value":"BBE33A34S4N4CDQV","created_at":"2026-07-05T10:26:17Z"},{"alias_kind":"pith_short_8","alias_value":"BBE33A34","created_at":"2026-07-05T10:26:17Z"}],"graph_snapshots":[{"event_id":"sha256:20592c1b689582d936b5eecb0bb2bc22d1a76d4550dfa2f4717121d1b2cae174","target":"graph","created_at":"2026-07-05T10:26:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.04969/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mobile robots are essential in applications such as autonomous delivery and hospitality services. Applying learning-based methods to address mobile robot tasks has gained popularity due to its robustness and generalizability. Traditional methods such as Imitation Learning (IL) and Reinforcement Learning (RL) offer adaptability but require large datasets, carefully crafted reward functions, and face sim-to-real gaps, making them challenging for efficient and safe real-world deployment. We propose an online human-in-the-loop learning method PVP4Real that combines IL and RL to address these issue","authors_text":"Bolei Zhou, Zhenghao Peng, Zhizheng Liu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T21:02:02Z","title":"Data-Efficient Learning from Human Interventions for Mobile Robots"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04969","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e0308e590cdfcf94442b7e3a605d241c25578d8562e270bad3b863715b96dd56","target":"record","created_at":"2026-07-05T10:26:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"af50092c68d5522046cfcfe41ed11231057271e3fc1e14f5fab838d8f914cf65","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T21:02:02Z","title_canon_sha256":"135e1325fb0f2c522ca60f2f3626433be895317d2f56da8c85f70a2a8cd6ce86"},"schema_version":"1.0","source":{"id":"2503.04969","kind":"arxiv","version":1}},"canonical_sha256":"0849bd837c971bc10e154704b178b89e49483ea0b7bda3e64b1d21ff89ff5718","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0849bd837c971bc10e154704b178b89e49483ea0b7bda3e64b1d21ff89ff5718","first_computed_at":"2026-07-05T10:26:17.603168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:26:17.603168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sFkLOUybmKj0W4GLxnkYJh64BaULQggsBBWR2t8swVcJbQBDk4NYYZ7oilqcq/Zlam5XPUGSXGEh9hRck7j+Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:26:17.603637Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04969","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0308e590cdfcf94442b7e3a605d241c25578d8562e270bad3b863715b96dd56","sha256:20592c1b689582d936b5eecb0bb2bc22d1a76d4550dfa2f4717121d1b2cae174"],"state_sha256":"3066f605285a5c89af5464a964d49654a6a43e3a2b6e0a8fd50017170c959567"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tM2ak3jGbm1tU+5BmhcOKyVx07V4APlZ65Fq2WaztyCkcPutjQH+ukMZmpP7vd7oc/CVnhQkSNfysqIg2NDuBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T05:56:50.631543Z","bundle_sha256":"887d28b4bd296524ae05c4f2af3f46eacdbaa4a27b52098eb70edfa30bde0aa6"}}