Gpt 6 Astra shows promise for robot tasks needing understanding
An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond
Computer Vision and Pattern Recognition
Summary
Robots often use two systems: one for quick actions and one for smart planning. This paper explores if a language model can directly control robot actions without extra training. The researchers tested three large language models on 42 robot manipulation tasks. They found GPT-6 Astra performed much better than others on tasks needing understanding but struggled with precise or quick movements. This suggests language models could help robots do some tasks but still have limits with fine control.
What this means in practice
- •For robotics engineers: Use language models like GPT-6 Astra to improve robot task planning where semantic understanding is key but precision is less critical.
- •For automation system integrators: Integrate general-purpose language model policies into systems performing manipulation tasks that require understanding of commands but not exact physical control.
Authors
Wenbo Zhang, Kaixuan Wang, Yutao Ouyang, Xiaoyu Huang, Liyang Li, Kailun Su, Weiyang Jin, Wenhao Chai, Haotian Liang, Zhiyang Dou, Yue Chen, Tianxing Chen
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.