An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond

Wenbo Zhang; Kaixuan Wang; Yutao Ouyang; Xiaoyu Huang; Liyang Li; Kailun Su; Weiyang Jin; Wenhao Chai; Haotian Liang; Zhiyang Dou; Yue Chen; Tianxing Chen

arXiv · 2609.24170

An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond

Wenbo Zhang·Kaixuan Wang·Yutao Ouyang·Xiaoyu Huang·Liyang Li·Kailun Su·Weiyang Jin·Wenhao Chai·Haotian Liang·Zhiyang Dou·Yue Chen·Tianxing Chen

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Abstract

Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, whereas System 2 is often instantiated as a vision-enabled language model for high-level planning. We ask whether a large language model (LLM) can act as the policy for robot manipulation without task-specific finetuning. We call this setting LLM as policy. We evaluate three LLMs on all 42 RoboDojo tasks and compare their scores with 40 public policies. Astra and GPT-5.5 use the official 50-episode-per-task protocol; DeepSeek-Flash uses 10 episodes per task. GPT-6 Astra achieves 22.48% average success rate and 28.97 Score over 2,100 trials, ranking above every public entry. Yet GPT-5.5 and DeepSeek-Flash reach only 0.88% and 1.92% average success rate with the same post-processing. We find that Astra exhibits a sharply polarized capability profile. It generalizes well to tasks that require semantic understanding but not high-precision control. In contrast, it performs poorly on tasks that require precision, dynamic control, or complex bimanual coordination. In-context experiments show no aggregate benefit from one-shot demonstrations, while selected interaction traces show within-episode corrections under perturbations. Overall, the evaluated LLMs vary substantially in manipulation performance. Astra stands out and provides initial evidence for the potential of a general-purpose manipulation model, although reliable precision and dynamic control remain limitations in the evaluated setting.

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Wenbo Zhang, Kaixuan Wang, Yutao Ouyang, Xiaoyu Huang, Liyang Li, Kailun Su, Weiyang Jin, Wenhao Chai, Haotian Liang, Zhiyang Dou, Yue Chen, Tianxing Chen. 2026-09-21. An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond. https://arxiv.org/abs/2609.24170

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