GPT-6-Astra's Embodied Intelligence Put to the Test, Traditional Benchmarks Score Perfectly, Real Robots Hit Snags
GPT-6-Astra具身能力遭大考，传统基准拿满分，真机频碰壁
This research paper evaluates GPT-6-Astra's embodied intelligence capabilities across 12 simulated benchmarks and 8 real-world robot platforms. While it achieves high scores in structured tasks like LIBERO-Long++ and SIMPLE, real-world execution reveals significant limitations, including safety issues, mechanical load constraints, and failure to handle complex, unstructured tasks. The study highlights the gap between simulation and physical deployment, and proposes improvements through better execution systems and task-specific adaptations.
Source: Chinese robotics — Robot learning research · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
87.3%
View original evidence
对于更复杂的灵巧操作，GPT-6-Astra 在 DexJoCo 上取得 87.3% 成功率，在 RoboDojo 上达到 81.9%。
Open source S19Reported success rate
27.6%
View original evidence
这一调整带来的效果立竿见影，以 RoboDojo 基准为例，其上的成功率从标准预算下的 27.6% 提高到 10× 预算下的 81.9%。
Open source S24
- paper: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
技术报告地址：https://www.sensenova.ai/has-gpt-6-astra-achieved-embodied-intelligence 有意思的是，同一个 GPT-6-Astra，在简单解除限制之后，在多项传统具身评测中交出了满分或接近满分的成绩：其中在长程组合操作评测基准 LIBERO-Long++ 上达到 100%，在考察操作鲁棒性和泛化能力的 LIBERO-Pro 上达到 99.3%，人形机器人基准 SIMPLE 上达到 98.8%，RoboDojo 上也可以达到 81.9%。
Open source S7
除了上文已经提到的 LIBERO-Long++、LIBERO-Pro 和 SIMPLE，在超长程机器人操作基准 LIBERO-Ultra-Long 上为 98.9%。
Open source S18
对于更复杂的灵巧操作，GPT-6-Astra 在 DexJoCo 上取得 87.3% 成功率，在 RoboDojo 上达到 81.9%。
Open source S19
这一调整带来的效果立竿见影，以 RoboDojo 基准为例，其上的成功率从标准预算下的 27.6% 提高到 10× 预算下的 81.9%。
Open source S24
Source：Chinese robotics — Robot learning research · c114.net.cn