{"paper":{"title":"Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"DAJI learns dynamics-aligned joint intents so language commands produce humanoid actions that are both immediate and anticipatory of future contacts and balance shifts.","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Haozhe Jia, Honglei Jin, Jianfei Song, Kuimou Yu, Lei Wang, Shaofeng Liang, Shuxu Jin, Wenshuo Chen, Youcheng Fan, Yuan Zhang, Yutao Yue, Zinuo Zhang","submitted_at":"2026-05-14T06:05:24Z","abstract_excerpt":"Natural language is an intuitive interface for humanoid robots, yet streaming whole-body control requires control representations that are executable now and anticipatory of future physical transitions. Existing language-conditioned humanoid systems typically generate kinematic references that a low-level tracker must repair reactively, or use latent/action policies whose outputs do not explicitly encode upcoming contact changes, support transfers, and balance preparation. We propose \\textbf{DAJI} (\\emph{Dynamics-Aligned Joint Intent}), a hierarchical framework that learns an anticipatory join"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"DAJI achieves strong results in anticipatory latent learning, single-instruction generation, and streaming instruction following, reaching 94.42% rollout success on HumanML3D-style generation and 0.152 subsequence FID on BABEL.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That distilling a future-aware teacher policy into a deployable diffusion action policy via student-driven rollouts preserves the anticipatory properties without substantial degradation in closed-loop performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DAJI learns future-aware joint intents from language to enable proactive humanoid control, reporting 94.42% rollout success on HumanML3D-style tasks and 0.152 subsequence FID on BABEL.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DAJI learns dynamics-aligned joint intents so language commands produce humanoid actions that are both immediate and anticipatory of future contacts and balance shifts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4f373da91245bcdc67226c56822d6ca3e94b2bdc8b8c1810cba8c7613af494bf"},"source":{"id":"2605.14417","kind":"arxiv","version":1},"verdict":{"id":"2179876d-1c20-40a3-a8c7-6f0105617676","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:11:06.582991Z","strongest_claim":"DAJI achieves strong results in anticipatory latent learning, single-instruction generation, and streaming instruction following, reaching 94.42% rollout success on HumanML3D-style generation and 0.152 subsequence FID on BABEL.","one_line_summary":"DAJI learns future-aware joint intents from language to enable proactive humanoid control, reporting 94.42% rollout success on HumanML3D-style tasks and 0.152 subsequence FID on BABEL.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That distilling a future-aware teacher policy into a deployable diffusion action policy via student-driven rollouts preserves the anticipatory properties without substantial degradation in closed-loop performance.","pith_extraction_headline":"DAJI learns dynamics-aligned joint intents so language commands produce humanoid actions that are both immediate and anticipatory of future contacts and balance shifts."},"references":{"count":48,"sample":[{"doi":"","year":1992,"title":"J.Massion,“Movement,postureandequilibrium:Interaction and coordination”,Progress in neurobiology, vol. 38, no. 1, pp.35–56,1992","work_id":"6cb63c6e-69c1-4aaf-b94b-a8f2b21703eb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"S.BouissetandM.-C.Do,“Posture,dynamicstability,andvol- untarymovement”,NeurophysiologieClinique/ClinicalNeuro- physiology,vol.38,no.6,pp.345–362,2008","work_id":"0094cddd-6532-48b3-9f3a-5d337c17915e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/iros.2012.6386109","year":2012,"title":"MuJoCo: A physics engine for model-based control","work_id":"5f5e6f59-3edd-4ba3-9cc2-7e7483d4d151","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"The kit motion- languagedataset","work_id":"8c1bfffa-65b6-4898-b933-cf4a05104c0b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Proximal Policy Optimization Algorithms","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","ref_index":5,"cited_arxiv_id":"1707.06347","is_internal_anchor":true}],"resolved_work":48,"snapshot_sha256":"ad895c95f94df84021534a2baf35eed00455eba9ee26fcdbd0f0a8fc72c8d311","internal_anchors":8},"formal_canon":{"evidence_count":2,"snapshot_sha256":"37413b106950f3efe441ef4d8aa49686a1c6e3b8f70552feede16d596d2724b4"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}