After GPT-6, embodied foundation models box themselves in
GPT-6之后，具身大模型画地为牢
B · Useful, limited QbitAI trade feature built on interviews with the first author of the RoboDojo benchmark and several robotics founders; useful industry context, but the score gains are simulation-only and the job-cut reports are unattributed.
Summary based on the full text·Original: about 9 minutes to read
In short
A 8 November 2026 feature by QbitAI (量子位), carried by 36Kr, on how OpenAI's GPT-6 Astra unsettled Chinese embodied-AI companies that train their own robot foundation models. In the RoboDojo evaluation, a general model without task-specific post-training or demonstrations handled in-context learning, out-of-distribution correction and open-vocabulary manipulation, three problems embodied start-ups had built pitches around. Real-robot tests were halted for safety and API latency is high, so the article ends on economics: use rules or small models where tasks are stable and general models where tasks keep changing.
Key numbers
- 20-30 to 50-60RoboDojo scores of teams' models after combining them with GPT-6 Astra-based approaches
- 80 to 90interviewee's analogy: in-house embodied models add 0.1-0.2 points at 80, Astra jumped to 90 at once
- about 2 monthstime since GPT-6 Astra's release, with GPT-6.2 Astra expected soon
As reported by the source.
Chinese·Web page·opens 36kr.com
What it reports
- Industry reports say some embodied-AI companies have started cutting algorithm jobs; no companies are named.
- Per Chen Tianxing (HKU MMLab, first author of RoboTwin/RoboDojo), teams that had trained native embodied models now submit solutions using general models via Robot-Use-Agent; scores rose from 20-30 to 50-60.
- Astra results: image or text demonstrations raised success over none; it adapted after deliberate camera and coordinate changes; it re-planned a grasp when an object moved; it followed varied phrasing without task post-training.
- The RoboDojo report states Astra's real-robot tests were stopped over safety, and simulation scores do not equal real-world productivity.
- Weaknesses named: continuous contact, multi-step physical manipulation, force and deformation (per founders Xu Huazhe and Wang Qian), and high cloud API latency for closed-loop control. An engineer notes slow inference can still allow fast low-level control for plannable tasks such as parcel sorting.
- Chen's framing: open tasks lean on general models, stable tasks on rules or light policies, the middle on scene-optimised models. OpenAI has been hiring heavily for robotics roles this year.
- Mood: "investors have stopped investing in embodied AI", which the article itself calls possibly exaggerated.
How they got it
Journalistic piece drawing on the RoboDojo team's early evaluation report and interviews with its first author, two founders and anonymous engineers.
Limits
- Benchmark results are zero-shot simulation (RoboDojo-Sim) only; no real-robot numbers.
- Job cuts and investor sentiment are hearsay with no names or figures.
- Trade-media tone; score gains are quoted without the full table.
Why it matters for companies
For buyers of robotics the useful point is the cost test: on a fixed production line, success rate, takt, stability and price per call matter more than general intelligence, so a cheap specialised model may beat an expensive general one. For anyone funding in-house foundation models the story is a warning that a general model can absorb years of specialised work in one release; investment should go where general models remain weak, here physical contact, hardware control and deployment.
Key passages in translation
Chinese original
具身自研模型还在80分上不断加0.1、加0.2，GPT-6 Astra已经一次性从80分跳到了90分。
In-house embodied models keep adding 0.1 or 0.2 at 80 points; GPT-6 Astra jumped from 80 to 90 in one go.
Chinese original
Astra的真机测试曾因安全问题中止。
Astra's real-robot tests were once halted over safety problems.
Chinese original
如果花几毛钱调用一个模型就能稳定完成任务，为什么非要花几块钱调用GPT-6 Astra？
If a model costing a few cents per call can do the task reliably, why pay a few yuan to call GPT-6 Astra?
Summary based on the full text. Read the original for the full argument and data.Read the original on 36kr.com
- Tags
- Embodied AI, Labour