{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JNHCX2WD63DBITTZ6EOITNXYNH","short_pith_number":"pith:JNHCX2WD","schema_version":"1.0","canonical_sha256":"4b4e2beac3f6c6144e79f11c89b6f869d22441963b1673d86cb290bac2b2cfc3","source":{"kind":"arxiv","id":"2403.10083","version":1},"attestation_state":"computed","paper":{"title":"HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Liguo Chen, Songhao Piao, Wei Li, Wenzheng Chi, Xinyu Zhou","submitted_at":"2024-03-15T07:54:36Z","abstract_excerpt":"Crowd navigation has received significant research attention in recent years, especially DRL-based methods. While single-robot crowd scenarios have dominated research, they offer limited applicability to real-world complexities. The heterogeneity of interaction among multiple agent categories, like in decentralized multi-robot pedestrian scenarios, are frequently disregarded. This \"interaction blind spot\" hinders generalizability and restricts progress towards robust navigation algorithms. In this paper, we propose a heterogeneous relational deep reinforcement learning(HeR-DRL), based on custo"},"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":"2403.10083","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-03-15T07:54:36Z","cross_cats_sorted":[],"title_canon_sha256":"7fbb3338334e30822a06559500c6e62d5dd602a448ae87ef2fd724daf4dd1174","abstract_canon_sha256":"3172f91a7a4a05b03776560e89c66a162f6b431cafa766a3ae09224befbbc138"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:29.998533Z","signature_b64":"4la7cUiktZnImUqlTkTJAc7HPVXJN2RLiO+vfc1ZV7YSEQAhHNaAgc/bJRz9aSvlqco8X8vOWvdAtETQ0PLtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b4e2beac3f6c6144e79f11c89b6f869d22441963b1673d86cb290bac2b2cfc3","last_reissued_at":"2026-07-05T07:56:29.998141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:29.998141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Liguo Chen, Songhao Piao, Wei Li, Wenzheng Chi, Xinyu Zhou","submitted_at":"2024-03-15T07:54:36Z","abstract_excerpt":"Crowd navigation has received significant research attention in recent years, especially DRL-based methods. While single-robot crowd scenarios have dominated research, they offer limited applicability to real-world complexities. The heterogeneity of interaction among multiple agent categories, like in decentralized multi-robot pedestrian scenarios, are frequently disregarded. This \"interaction blind spot\" hinders generalizability and restricts progress towards robust navigation algorithms. In this paper, we propose a heterogeneous relational deep reinforcement learning(HeR-DRL), based on custo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10083","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/2403.10083/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":"2403.10083","created_at":"2026-07-05T07:56:29.998204+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10083v1","created_at":"2026-07-05T07:56:29.998204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10083","created_at":"2026-07-05T07:56:29.998204+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNHCX2WD63DB","created_at":"2026-07-05T07:56:29.998204+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNHCX2WD63DBITTZ","created_at":"2026-07-05T07:56:29.998204+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNHCX2WD","created_at":"2026-07-05T07:56:29.998204+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/JNHCX2WD63DBITTZ6EOITNXYNH","json":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH.json","graph_json":"https://pith.science/api/pith-number/JNHCX2WD63DBITTZ6EOITNXYNH/graph.json","events_json":"https://pith.science/api/pith-number/JNHCX2WD63DBITTZ6EOITNXYNH/events.json","paper":"https://pith.science/paper/JNHCX2WD"},"agent_actions":{"view_html":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH","download_json":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH.json","view_paper":"https://pith.science/paper/JNHCX2WD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10083&json=true","fetch_graph":"https://pith.science/api/pith-number/JNHCX2WD63DBITTZ6EOITNXYNH/graph.json","fetch_events":"https://pith.science/api/pith-number/JNHCX2WD63DBITTZ6EOITNXYNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH/action/storage_attestation","attest_author":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH/action/author_attestation","sign_citation":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH/action/citation_signature","submit_replication":"https://pith.science/pith/JNHCX2WD63DBITTZ6EOITNXYNH/action/replication_record"}},"created_at":"2026-07-05T07:56:29.998204+00:00","updated_at":"2026-07-05T07:56:29.998204+00:00"}