{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:TY4HGDTIUT4RGC35RIPKDOS7TQ","short_pith_number":"pith:TY4HGDTI","schema_version":"1.0","canonical_sha256":"9e38730e68a4f9130b7d8a1ea1ba5f9c20f790d64b6c40e0cd5d585da2112a59","source":{"kind":"arxiv","id":"2010.14838","version":3},"attestation_state":"computed","paper":{"title":"Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adarsh Jagan Sathyamoorthy, Dinesh Manocha, Nithish Kumar, Utsav Patel","submitted_at":"2020-10-28T09:21:56Z","abstract_excerpt":"We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinfo"},"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":"2010.14838","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-28T09:21:56Z","cross_cats_sorted":[],"title_canon_sha256":"70e97b105e6f003904b44754aecabc1a002f33b7b10ac4c439063ef4c120fbe4","abstract_canon_sha256":"a583f534f5f097424d65444e12a65f090078c21f6b8e7b94e87a2d2c732e34cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:54:48.091802Z","signature_b64":"VKLmMOPBsCTWwKQXGYeRWnKXmsAO0tqf9USrGUsYLF3BIrjwLJQFT5dQOfYYFGwOi0Rr1PLoRvIFYKl28knDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e38730e68a4f9130b7d8a1ea1ba5f9c20f790d64b6c40e0cd5d585da2112a59","last_reissued_at":"2026-07-05T01:54:48.091368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:54:48.091368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adarsh Jagan Sathyamoorthy, Dinesh Manocha, Nithish Kumar, Utsav Patel","submitted_at":"2020-10-28T09:21:56Z","abstract_excerpt":"We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.14838","kind":"arxiv","version":3},"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/2010.14838/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":"2010.14838","created_at":"2026-07-05T01:54:48.091428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.14838v3","created_at":"2026-07-05T01:54:48.091428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.14838","created_at":"2026-07-05T01:54:48.091428+00:00"},{"alias_kind":"pith_short_12","alias_value":"TY4HGDTIUT4R","created_at":"2026-07-05T01:54:48.091428+00:00"},{"alias_kind":"pith_short_16","alias_value":"TY4HGDTIUT4RGC35","created_at":"2026-07-05T01:54:48.091428+00:00"},{"alias_kind":"pith_short_8","alias_value":"TY4HGDTI","created_at":"2026-07-05T01:54:48.091428+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02593","citing_title":"A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ","json":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ.json","graph_json":"https://pith.science/api/pith-number/TY4HGDTIUT4RGC35RIPKDOS7TQ/graph.json","events_json":"https://pith.science/api/pith-number/TY4HGDTIUT4RGC35RIPKDOS7TQ/events.json","paper":"https://pith.science/paper/TY4HGDTI"},"agent_actions":{"view_html":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ","download_json":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ.json","view_paper":"https://pith.science/paper/TY4HGDTI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.14838&json=true","fetch_graph":"https://pith.science/api/pith-number/TY4HGDTIUT4RGC35RIPKDOS7TQ/graph.json","fetch_events":"https://pith.science/api/pith-number/TY4HGDTIUT4RGC35RIPKDOS7TQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ/action/storage_attestation","attest_author":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ/action/author_attestation","sign_citation":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ/action/citation_signature","submit_replication":"https://pith.science/pith/TY4HGDTIUT4RGC35RIPKDOS7TQ/action/replication_record"}},"created_at":"2026-07-05T01:54:48.091428+00:00","updated_at":"2026-07-05T01:54:48.091428+00:00"}