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SCRIPT model advances humanoid control with language and diffusion

Researchers have developed SCRIPT, a novel diffusion policy designed for controlling physics-based humanoids using natural language instructions. This method utilizes a Joint Action-State-Text Diffusion Transformer (JAST-DiT) to integrate language semantics with control dynamics. SCRIPT also incorporates a nonlinear history conditioning mechanism for stable long-horizon control and employs Reinforcement Learning with Hybrid Rewards (RLHR) for enhanced performance. AI

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IMPACT Introduces a new framework for language-driven humanoid control, potentially enabling more sophisticated embodied agents.

RANK_REASON The cluster contains a research paper detailing a new model and methodology for humanoid control. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 · Jingyan Zhang, Han Liang, Ruichi Zhang, Bin Li, Juze Zhang, Xin Chen, Jingya Wang, Lan Xu, Jingyi Yu ·

    SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-Based Humanoid Control

    arXiv:2605.22894v1 Announce Type: cross Abstract: Controlling physics-based humanoids from natural-language instructions is a critical step toward general-purpose embodied agents. However, existing methods remain constrained by a tension between semantic expressiveness and physic…