# SenseTime — 商汤实测 GPT-6-Astra 并开源 SenseNova-RoboRSI：具身智能下一步怎么走？

- Company: SenseTime (sensetime.com)
- Announced: 2026-10-18T16:00:52+00:00
- Category: open-weight-release
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://www.sensetime.com/cn/news/gpt-6-astra-sensenova-roborsi-20261019-1513
- Record: https://forck.live/items/24404-gpt-6-astra-sensenova-roborsi
- Title in English (translated by forck.live from zh): SenseTime tests GPT-6-Astra and open-sources SenseNova-RoboRSI: where does embodied intelligence go next?
- Subject: SenseNova / 商汤
- Open weights: yes
- Models affected: SenseNova-RoboRSI-27B-Preview, GPT-6-Astra, 𝝅0.5, Sonic

SenseTime has open-sourced SenseNova-RoboRSI, a harness for reproducing embodied-AI evaluations, plus a 27B embodied model it says cuts task cost to 1/50 of GPT-6-Astra. The release follows a study of GPT-6-Astra across 12 simulator benchmarks and 8 real robots, which found that lifting step limits and allowing in-context learning pushed the model close to saturation on several leaderboards. In RoboDojo, nine tasks that had failed five times out of five succeeded five times out of five once the step cap was raised tenfold, without retraining. But the study puts the cost of that success in view: stacking bowls took about 131 minutes and roughly $19 of estimated inference, while putting a pen in a holder took 263 minutes and about $55. SenseTime says SenseNova-RoboRSI-27B-Preview scored 26.13 on RoboDojo at a 20.83% success rate, and on RoboCasa365 raised success from 26.0% to 46.5% while cutting task token cost by 73%. The harness packages benchmarks and their dependencies as Docker images behind a unified HTTP API, so researchers can reproduce end-to-end evaluations from a configuration file instead of deploying simulators by hand. SenseTime also reports that on real hardware GPT-6-Astra could operate all eight tested robots and adjust its own viewing angle, but still struggled with deformable materials, fine control and long-horizon navigation.

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