An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond
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...
| Authors | Wenbo Zhang, Kaixuan Wang, Yutao Ouyang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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