[2605.14417] Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control
[Submitted on 14 May 2026]
Title:Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control
View a PDF of the paper titled Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control, by Haozhe Jia and 10 other authors
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Abstract: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 joint-intent interface between language generation and closed-loop control. DAJI-Act distills a future-aware teacher into a deployable diffusion action policy through student-driven rollouts, while DAJI-Flow autoregressively generates future intent chunks from language and intent history. Experiments show that 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.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.14417 [cs.RO]
(or arXiv:2605.14417v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.14417
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haozhe Jia [view email] [v1] Thu, 14 May 2026 06:05:24 UTC (20,739 KB)
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