Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control

• URL: http://arxiv.org/abs/2605.14417v2
• Date: Wed, 20 May 2026 06:15:55 GMT
• Title: Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control
• Abstract summary: We propose textbfDAJI (emphDynamics-Aligned Joint Intent), a hierarchical framework that learns an anticipatory joint-intent interface between language generation and closed-loop control.<n>Experiments show that DAJI achieves strong results in anticipatory latent learning, single-instruction generation, and streaming instruction following.
• Score: 7.4507678595872795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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