用开源项目把 SenseNova-RoboRSI-27B-Preview 接入仿真机器人
商汤开源的 SenseNova-RoboRSI-27B-Preview 能接仿真机器人干活，LIBERO-Pro 成功率 96.1%，想玩机器人方向的可以照这个流程搭一套。
商汤的 SenseNova-RoboRSI-27B-Preview 是一个机器人视觉语言模型，可以接入仿真环境完成操作任务。工作流分四步：模型接收任务和相机图像、输出动作、仿真器执行并返回新观测、模型根据反馈继续决策。在 RoboDojo 基准上平均得分 26.13、任务成功率 20.83%；在 LIBERO-Pro 上直接控制成功率达 96.1%。这套流程可以用来对比不同模型在机器人任务上的表现，也能分析机器人失败的原因。
You can use this open project to connect a model to a simulated robot.
Then, you can measure how well the robot completes a task.
The basic loop is simple:
1. The model, SenseNova-RoboRSI-27B-Preview, receives a task and images from the robot's cameras.
2. It chooses a movement
3. The simulator executes that movement and returns new observations
4. The model uses that feedback to decide what to do next
Some of the model highlights:
• Inspects visual details and reasons about spatial relationships • Adjusts actions based on execution feedback • 26.13 average score and 20.83% task success on RoboDojo • 96.1% success rate with direct control on LIBERO-Pro
This is pretty useful for comparing models and understanding why a robot failed.
