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arXiv · 2610.14018

RoboHarness: A Simple Harness Outperforms VLA and World Actions Models

Bince Qu·Wei Chen·Bo Zhang
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Abstract

The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world. One straightforward approach is training the model to generate actions: collect robot-action data and video demonstration, refine LLM architecture with an action head, and train a vision-language-action (VLA) model at scale. However, we argue that coding agents have the potential to realize generalist embodied manipulation. All they need is a simple yet effective robot harness. We present RoboHarness: a robotic harness that gives LLM agents a visual-geometric control panel so that they can directly understand and invoke embodied tasks. With RoboHarness, Qwen3.8-Flash-Next outperforms state-of-the-art VLA models on BEHAVIOR Challenge 2025 tasks. The implementation will be released as open source.

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Bince Qu, Wei Chen, Bo Zhang. 2026-09-29. RoboHarness: A Simple Harness Outperforms VLA and World Actions Models. https://arxiv.org/abs/2610.14018

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