{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KL6UWGNCPZPZ5FQP7ZCZOZUQNT","short_pith_number":"pith:KL6UWGNC","schema_version":"1.0","canonical_sha256":"52fd4b19a27e5f9e960ffe459766906cdf5afd06fe017351835c727f21c1c0ab","source":{"kind":"arxiv","id":"2502.10894","version":1},"attestation_state":"computed","paper":{"title":"Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Gabriel B. Margolis, Martin Peticco, Nolan Fey, Pulkit Agrawal","submitted_at":"2025-02-15T20:18:37Z","abstract_excerpt":"Achieving athletic loco-manipulation on robots requires moving beyond traditional tracking rewards - which simply guide the robot along a reference trajectory - to task rewards that drive truly dynamic, goal-oriented behaviors. Commands such as \"throw the ball as far as you can\" or \"lift the weight as quickly as possible\" compel the robot to exhibit the agility and power inherent in athletic performance. However, training solely with task rewards introduces two major challenges: these rewards are prone to exploitation (reward hacking), and the exploration process can lack sufficient direction."},"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":"2502.10894","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-15T20:18:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b322fcee8fc03fddc877f0995dc851a6e84553319e9b405deb08a9fd113701b3","abstract_canon_sha256":"bae6defa97156d1c95b6dea25c63ab2b2f7e3493c9abaebc63254da8b7f1dc33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:59.772882Z","signature_b64":"eREPd/AxvhTxWyE2mjnjP5HOaqEDRlpXBkbKZojqb177F6QJj/0pTrppIwev6N0cHGgb/AdTTDJnm3g4cDOiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52fd4b19a27e5f9e960ffe459766906cdf5afd06fe017351835c727f21c1c0ab","last_reissued_at":"2026-07-05T10:14:59.772394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:59.772394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Gabriel B. Margolis, Martin Peticco, Nolan Fey, Pulkit Agrawal","submitted_at":"2025-02-15T20:18:37Z","abstract_excerpt":"Achieving athletic loco-manipulation on robots requires moving beyond traditional tracking rewards - which simply guide the robot along a reference trajectory - to task rewards that drive truly dynamic, goal-oriented behaviors. Commands such as \"throw the ball as far as you can\" or \"lift the weight as quickly as possible\" compel the robot to exhibit the agility and power inherent in athletic performance. However, training solely with task rewards introduces two major challenges: these rewards are prone to exploitation (reward hacking), and the exploration process can lack sufficient direction."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.10894","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/2502.10894/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":"2502.10894","created_at":"2026-07-05T10:14:59.772448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.10894v1","created_at":"2026-07-05T10:14:59.772448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.10894","created_at":"2026-07-05T10:14:59.772448+00:00"},{"alias_kind":"pith_short_12","alias_value":"KL6UWGNCPZPZ","created_at":"2026-07-05T10:14:59.772448+00:00"},{"alias_kind":"pith_short_16","alias_value":"KL6UWGNCPZPZ5FQP","created_at":"2026-07-05T10:14:59.772448+00:00"},{"alias_kind":"pith_short_8","alias_value":"KL6UWGNC","created_at":"2026-07-05T10:14:59.772448+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24466","citing_title":"FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07118","citing_title":"QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05880","citing_title":"TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2606.24466","citing_title":"FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06218","citing_title":"TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15713","citing_title":"Learning Dynamic Pick-and-Place for a Legged Manipulator","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10351","citing_title":"Trajectory-based actuator identification via differentiable simulation","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT","json":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT.json","graph_json":"https://pith.science/api/pith-number/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/graph.json","events_json":"https://pith.science/api/pith-number/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/events.json","paper":"https://pith.science/paper/KL6UWGNC"},"agent_actions":{"view_html":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT","download_json":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT.json","view_paper":"https://pith.science/paper/KL6UWGNC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.10894&json=true","fetch_graph":"https://pith.science/api/pith-number/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/graph.json","fetch_events":"https://pith.science/api/pith-number/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/action/storage_attestation","attest_author":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/action/author_attestation","sign_citation":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/action/citation_signature","submit_replication":"https://pith.science/pith/KL6UWGNCPZPZ5FQP7ZCZOZUQNT/action/replication_record"}},"created_at":"2026-07-05T10:14:59.772448+00:00","updated_at":"2026-07-05T10:14:59.772448+00:00"}