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arXiv · 2609.32250

RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots

Yujia Zeng·Chensheng Peng·Yuxin Chen·Alex Shao·Nathan Jew·Masayoshi Tomizuka
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Abstract

Sign-language interpretation in public communication relies on qualified professional interpreters and can be difficult to scale, motivating robotic signing as a complementary accessibility interface. We present RoBoSTAR, a text-conditioned sign language production (SLP) framework for generating human-centric sign motion that can be retargeted for robotic execution, with speech supported optionally through an external ASR front end. Conventional autoregressive approaches flatten motion into a single full-resolution token sequence, forcing long-range and local dependencies to be modeled at a uniform temporal granularity. RoBoSTAR instead combines part-wise Finite Scalar Quantization with next-scale autoregression, generating motion over progressively finer temporal resolutions while predicting synchronized body and hand tokens in parallel within each step. This coarse-to-fine formulation provides compact long-range context before progressively refining motion details, while self-conditioning and context corruption improve robustness to cross-scale prediction errors. The generated motion is subsequently retargeted for physical humanoid execution. Extensive qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of RoBoSTAR.

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Yujia Zeng, Chensheng Peng, Yuxin Chen, Alex Shao, Nathan Jew, Masayoshi Tomizuka. 2026-09-26. RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots. https://arxiv.org/abs/2609.32250

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