新论文测试 GPT-6-Astra 在 12 个机器人基准上的表现
New paper finds that GPT-6-Astra nearly saturates easy robot benchmarks but still fails to verify physical success and recover from irreversible mistakes.
一篇新论文在 12 个机器人基准上测试 GPT-6-Astra，它在 LIBERO 套件上接近饱和，在 RoboDojo 上达到 81.9%、在 DexJoCo 上达到 87.3%。但单个 LIBERO-Ultra-Long 回合消耗约 9M tokens、耗时近 2 小时，且在真实机器人上无法用更多步数换取成功。作者认为通用模型目前可作为研究用的机器人控制器，任务专用演示、高算力需求和物理限制仍阻碍部署。
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New paper finds that GPT-6-Astra nearly saturates easy robot benchmarks but still fails to verify physical success and recover from irreversible mistakes. And at a cost too high for real use.
Robot manipulation and navigation have been dominated by task-specific models. The question was whether 1 general model could handle them all without weight updates.
The authors tested GPT-6-Astra on 12 robot benchmarks. It studied training demos into a frozen knowledge package and got up to 10 times the usual action steps to recover.
It neared saturation on the LIBERO suite and reached 81.9% on RoboDojo and 87.3% on DexJoCo. But a single LIBERO-Ultra-Long episode used about 9M tokens and nearly 2 hours, and on real robots it could not trade more steps for success.
Treat general models as capable robot controllers for research, but expect task-specific demos, heavy compute, and physical limits to block deployment for now.
– arxiv. org/abs/2610.22471
Title: "Has GPT-6-Astra Achieved Embodied Intelligence?"