Rohan Paul (@rohanpaul_ai) · 2026/10/22 13:56:00
研究称 GPT-6-Astra 近饱和机器人基准但难验物理成功
最新论文评估了通用大模型 GPT-6-Astra 在具身智能任务中的表现，发现其在 LIBERO 等仿真基准上接近饱和，但在真实机器人部署中因无法验证物理成功状态且成本过高而失效。该研究揭示了当前通用模型作为机器人控制器的局限性：虽能处理多任务，但难以从不可逆错误中恢复，且单次长程任务消耗约 900 万 Token，阻碍了实际落地。
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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?"