SenseTime Open-Sources SenseNova-RoboRSI Framework and 27B Embodied Model

SenseTime has open-sourced the SenseNova-RoboRSI framework and a 27B embodied model, while also releasing an empirical study on embodied intelligence for GPT-6-Astra. The study covers 12 simulation benchmarks and 8 real robot platforms. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks, with RoboDojo's success rate rising from 27.6% at 1x budget to 81.9% at 10x, and DexJoCo reaching 87.3%, compared with the previous SOTA of 40.3%. The study also notes that simple tasks such as putting a pen into a pen holder cost about $55 in inference, and that real robots overheat and wait too long; the conclusion is that GPT-6-Astra has essentially crossed the threshold of embodied capability, but practical-grade embodied intelligence has not yet arrived. The open-source framework packages each benchmark as an independent Docker image and provides environment interaction and scoring through a unified HTTP API; the 27B model raises the success rate on RoboCasa365 from 26.0% to 46.5%, reduces task token overhead by 73%, and costs about 1/50 of GPT-6-Astra per completed task.

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1. Report date Oct 21· 2 reports

   SenseTime open-sources SenseNova-RoboRSI framework and 27B embodied model

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