{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HZB74YG2WHGA5WBK7WEG2UWPJK","short_pith_number":"pith:HZB74YG2","schema_version":"1.0","canonical_sha256":"3e43fe60dab1cc0ed82afd886d52cf4aa532fcfd5df50296bb15cd783551a2f9","source":{"kind":"arxiv","id":"2506.14770","version":2},"attestation_state":"computed","paper":{"title":"GMT: General Motion Tracking for Humanoid Whole-Body Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Mazeyu Ji, Xiaolong Wang, Xuanbin Peng, Xue Bin Peng, Xuxin Cheng, Zixuan Chen","submitted_at":"2025-06-17T17:59:33Z","abstract_excerpt":"The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixt"},"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":"2506.14770","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-06-17T17:59:33Z","cross_cats_sorted":[],"title_canon_sha256":"e2d4cdc6cceee2840628d41a1146d07de497fb394e55dc322fc5777a31145e6f","abstract_canon_sha256":"dc27b20797559a5ceea2bea7a1d765fceb71b5768c6354d61c6f7ba8fb03980d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:36.715390Z","signature_b64":"CIqbgnt7stCxfjbcEbjqh0q83BPd2PCw0WGPSe/8B5vXCxvyoJFCxqOayJb0flccjUEyxazv+8eYQ8qk+YPNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e43fe60dab1cc0ed82afd886d52cf4aa532fcfd5df50296bb15cd783551a2f9","last_reissued_at":"2026-07-05T12:04:36.714873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:36.714873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GMT: General Motion Tracking for Humanoid Whole-Body Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Mazeyu Ji, Xiaolong Wang, Xuanbin Peng, Xue Bin Peng, Xuxin Cheng, Zixuan Chen","submitted_at":"2025-06-17T17:59:33Z","abstract_excerpt":"The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14770","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/2506.14770/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":"2506.14770","created_at":"2026-07-05T12:04:36.714935+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14770v2","created_at":"2026-07-05T12:04:36.714935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14770","created_at":"2026-07-05T12:04:36.714935+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZB74YG2WHGA","created_at":"2026-07-05T12:04:36.714935+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZB74YG2WHGA5WBK","created_at":"2026-07-05T12:04:36.714935+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZB74YG2","created_at":"2026-07-05T12:04:36.714935+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":43,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"2607.07370","citing_title":"Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2607.08742","citing_title":"ContactMimic: Humanoid Object Interaction via Contact Control","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2607.07370","citing_title":"Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2607.06052","citing_title":"ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2607.06438","citing_title":"WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2606.25706","citing_title":"Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26392","citing_title":"MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.23680","citing_title":"CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2606.23565","citing_title":"HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22174","citing_title":"OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19633","citing_title":"CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion","ref_index":19,"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":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09286","citing_title":"VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06953","citing_title":"LIMMT: Less is More for Motion Tracking","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06493","citing_title":"HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03985","citing_title":"Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking","ref_index":4,"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":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03476","citing_title":"Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27581","citing_title":"SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23733","citing_title":"Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30645","citing_title":"VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30290","citing_title":"X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29209","citing_title":"AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28549","citing_title":"SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22272","citing_title":"Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK","json":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK.json","graph_json":"https://pith.science/api/pith-number/HZB74YG2WHGA5WBK7WEG2UWPJK/graph.json","events_json":"https://pith.science/api/pith-number/HZB74YG2WHGA5WBK7WEG2UWPJK/events.json","paper":"https://pith.science/paper/HZB74YG2"},"agent_actions":{"view_html":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK","download_json":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK.json","view_paper":"https://pith.science/paper/HZB74YG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14770&json=true","fetch_graph":"https://pith.science/api/pith-number/HZB74YG2WHGA5WBK7WEG2UWPJK/graph.json","fetch_events":"https://pith.science/api/pith-number/HZB74YG2WHGA5WBK7WEG2UWPJK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK/action/storage_attestation","attest_author":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK/action/author_attestation","sign_citation":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK/action/citation_signature","submit_replication":"https://pith.science/pith/HZB74YG2WHGA5WBK7WEG2UWPJK/action/replication_record"}},"created_at":"2026-07-05T12:04:36.714935+00:00","updated_at":"2026-07-05T12:04:36.714935+00:00"}