{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D5XAGIUPO6IMXAOCS3IQXCB5SV","short_pith_number":"pith:D5XAGIUP","schema_version":"1.0","canonical_sha256":"1f6e03228f7790cb81c296d10b883d9546750235ee1559ad504c7e7032c09063","source":{"kind":"arxiv","id":"2502.01143","version":3},"attestation_state":"computed","paper":{"title":"ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Changliu Liu, Chaoyi Pan, Guannan Qu, Guanqi He, Guanya Shi, Jessica Hodgins, Jiashun Wang, Jiawei Gao, Kris Kitani, Linxi \"Jim\" Fan, Nikhil Sobanbab, Tairan He, Wenli Xiao, Yuanhang Zhang, Yuke Zhu, Zeji Yi, Zhengyi Luo, Zi Wang","submitted_at":"2025-02-03T08:22:46Z","abstract_excerpt":"Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and the real world. Existing approaches, such as system identification (SysID) and domain randomization (DR) methods, often rely on labor-intensive parameter tuning or result in overly conservative policies that sacrifice agility. In this paper, we present ASAP (Aligning Simulation and Real-World Physics), a two-stage framework designed to tackle th"},"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":"2502.01143","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-02-03T08:22:46Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"4db77002840e18b12004930b7dabfa10917b1826b20c229975b2ae1bbf66c4e2","abstract_canon_sha256":"bdfae35a9584f1f6e2e80c4ecab87e4c23440f693d1c29ce50eccc76dae71dd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:14.959985Z","signature_b64":"IS9v5eIAzlSM6fUpmHNQWmXXlVxLM+cOqOMEyFVc8cPqxR3PK+dK3NI24pNyvC1xz8kWIbRYxsyJl7oJPlt/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f6e03228f7790cb81c296d10b883d9546750235ee1559ad504c7e7032c09063","last_reissued_at":"2026-07-05T10:54:14.959497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:14.959497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Changliu Liu, Chaoyi Pan, Guannan Qu, Guanqi He, Guanya Shi, Jessica Hodgins, Jiashun Wang, Jiawei Gao, Kris Kitani, Linxi \"Jim\" Fan, Nikhil Sobanbab, Tairan He, Wenli Xiao, Yuanhang Zhang, Yuke Zhu, Zeji Yi, Zhengyi Luo, Zi Wang","submitted_at":"2025-02-03T08:22:46Z","abstract_excerpt":"Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and the real world. Existing approaches, such as system identification (SysID) and domain randomization (DR) methods, often rely on labor-intensive parameter tuning or result in overly conservative policies that sacrifice agility. In this paper, we present ASAP (Aligning Simulation and Real-World Physics), a two-stage framework designed to tackle th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01143","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/2502.01143/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":"2502.01143","created_at":"2026-07-05T10:54:14.959556+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01143v3","created_at":"2026-07-05T10:54:14.959556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01143","created_at":"2026-07-05T10:54:14.959556+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5XAGIUPO6IM","created_at":"2026-07-05T10:54:14.959556+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5XAGIUPO6IMXAOC","created_at":"2026-07-05T10:54:14.959556+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5XAGIUP","created_at":"2026-07-05T10:54:14.959556+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":48,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2607.07370","citing_title":"Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2607.08741","citing_title":"ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation","ref_index":45,"is_internal_anchor":true},{"citing_arxiv_id":"2607.07370","citing_title":"Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2606.26201","citing_title":"OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26741","citing_title":"PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22860","citing_title":"HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2607.02034","citing_title":"ComplexMimic: Human-Scene Interaction Imitation in Complex 3D Environments","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12814","citing_title":"Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12783","citing_title":"A Tutorial on World Models and Physical AI","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09286","citing_title":"VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08555","citing_title":"FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08495","citing_title":"EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07934","citing_title":"X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07118","citing_title":"QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06218","citing_title":"TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04829","citing_title":"M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03985","citing_title":"Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03536","citing_title":"Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27581","citing_title":"SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24592","citing_title":"MuGen: Multi-Skill Generative Locomotion Controller for Humanoid Robots","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27046","citing_title":"Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28476","citing_title":"FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25782","citing_title":"ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26879","citing_title":"Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27046","citing_title":"Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV","json":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV.json","graph_json":"https://pith.science/api/pith-number/D5XAGIUPO6IMXAOCS3IQXCB5SV/graph.json","events_json":"https://pith.science/api/pith-number/D5XAGIUPO6IMXAOCS3IQXCB5SV/events.json","paper":"https://pith.science/paper/D5XAGIUP"},"agent_actions":{"view_html":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV","download_json":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV.json","view_paper":"https://pith.science/paper/D5XAGIUP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01143&json=true","fetch_graph":"https://pith.science/api/pith-number/D5XAGIUPO6IMXAOCS3IQXCB5SV/graph.json","fetch_events":"https://pith.science/api/pith-number/D5XAGIUPO6IMXAOCS3IQXCB5SV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV/action/storage_attestation","attest_author":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV/action/author_attestation","sign_citation":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV/action/citation_signature","submit_replication":"https://pith.science/pith/D5XAGIUPO6IMXAOCS3IQXCB5SV/action/replication_record"}},"created_at":"2026-07-05T10:54:14.959556+00:00","updated_at":"2026-07-05T10:54:14.959556+00:00"}