{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NAXFMLUTF2B4H3SAX4LWDZWTFH","short_pith_number":"pith:NAXFMLUT","schema_version":"1.0","canonical_sha256":"682e562e932e83c3ee40bf1761e6d329cb346ad5a02d8b2a8aebc30f7b798d19","source":{"kind":"arxiv","id":"2504.20808","version":2},"attestation_state":"computed","paper":{"title":"SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Florian Vahl, Jan Gutsche, Jasper G\\\"uldenstein, Jianwei Zhang, J\\\"orn Griepenburg","submitted_at":"2025-04-29T14:21:08Z","abstract_excerpt":"This paper introduces SoccerDiffusion, a transformer-based diffusion model designed to learn end-to-end control policies for humanoid robot soccer directly from real-world gameplay recordings. Using data collected from RoboCup competitions, the model predicts joint command trajectories from multi-modal sensor inputs, including vision, proprioception, and game state. We employ a distillation technique to enable real-time inference on embedded platforms that reduces the multi-step diffusion process to a single step. Our results demonstrate the model's ability to replicate complex motion behavior"},"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":"2504.20808","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2025-04-29T14:21:08Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5bda87df7761721fa57857a62d72f6b08e3d534c2cbb4d92df1c5006b8633deb","abstract_canon_sha256":"a53fb4c98e0a2e9634add52bb3c6e2a0499295f221144b9f9dc92e682d972bb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:27.658601Z","signature_b64":"9Y8ZCTjerLVeysk3LH1jtYySLdIvHgZSy77zCA11EOlL/opeIk4JFVFX4ZBdgWF4z+OsO8oc51TNc8PqpCDlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"682e562e932e83c3ee40bf1761e6d329cb346ad5a02d8b2a8aebc30f7b798d19","last_reissued_at":"2026-07-05T11:31:27.658157Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:27.658157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Florian Vahl, Jan Gutsche, Jasper G\\\"uldenstein, Jianwei Zhang, J\\\"orn Griepenburg","submitted_at":"2025-04-29T14:21:08Z","abstract_excerpt":"This paper introduces SoccerDiffusion, a transformer-based diffusion model designed to learn end-to-end control policies for humanoid robot soccer directly from real-world gameplay recordings. Using data collected from RoboCup competitions, the model predicts joint command trajectories from multi-modal sensor inputs, including vision, proprioception, and game state. We employ a distillation technique to enable real-time inference on embedded platforms that reduces the multi-step diffusion process to a single step. Our results demonstrate the model's ability to replicate complex motion behavior"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20808","kind":"arxiv","version":2},"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/2504.20808/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":"2504.20808","created_at":"2026-07-05T11:31:27.658220+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.20808v2","created_at":"2026-07-05T11:31:27.658220+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20808","created_at":"2026-07-05T11:31:27.658220+00:00"},{"alias_kind":"pith_short_12","alias_value":"NAXFMLUTF2B4","created_at":"2026-07-05T11:31:27.658220+00:00"},{"alias_kind":"pith_short_16","alias_value":"NAXFMLUTF2B4H3SA","created_at":"2026-07-05T11:31:27.658220+00:00"},{"alias_kind":"pith_short_8","alias_value":"NAXFMLUT","created_at":"2026-07-05T11:31:27.658220+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.06571","citing_title":"Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH","json":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH.json","graph_json":"https://pith.science/api/pith-number/NAXFMLUTF2B4H3SAX4LWDZWTFH/graph.json","events_json":"https://pith.science/api/pith-number/NAXFMLUTF2B4H3SAX4LWDZWTFH/events.json","paper":"https://pith.science/paper/NAXFMLUT"},"agent_actions":{"view_html":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH","download_json":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH.json","view_paper":"https://pith.science/paper/NAXFMLUT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.20808&json=true","fetch_graph":"https://pith.science/api/pith-number/NAXFMLUTF2B4H3SAX4LWDZWTFH/graph.json","fetch_events":"https://pith.science/api/pith-number/NAXFMLUTF2B4H3SAX4LWDZWTFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH/action/storage_attestation","attest_author":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH/action/author_attestation","sign_citation":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH/action/citation_signature","submit_replication":"https://pith.science/pith/NAXFMLUTF2B4H3SAX4LWDZWTFH/action/replication_record"}},"created_at":"2026-07-05T11:31:27.658220+00:00","updated_at":"2026-07-05T11:31:27.658220+00:00"}