SenseTime open-sources SenseNova-RoboRSI framework and 27B embodied model
The SenseTime SenseNova joint research team published a technical report on arXiv, evaluating the embodied capabilities of GPT-6-Astra across 12 simulation benchmarks and 8 real robot platforms, while open-sourcing the SenseNova-RoboRSI execution framework and its accompanying 27B embodied model, claiming to reduce task completion costs to 1/50 of directly invoking GPT-6-Astra. According to SenseTime, RoboRSI-27B improved its success rate on RoboCasa365 from 26.0% to 46.5% while reducing token usage by 73.3%; these results come from its own evaluation. The article notes that the 1/50 figure measures cost compression for specific tasks such as stacking bowls and loading pens under extended-budget conditions, and awaits third-party retesting; long-horizon tasks and fine-grained control of real robots remain weak points.
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