RoboHarness: A Simple Harness Outperforms VLA and World Actions Models

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.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.14018 [cs.RO]

(or arXiv:2610.14018v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.14018 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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