SenseNova-RoboRSI 报告机器人任务测试结果

@rohanpaul_ai yeah the model is rarely the bottleneck once you can act. better tool loop beats a bigger brain for a lot of robot tasks

A stronger robot doesn't always require a stronger foundation model. Sometimes, it just needs a better way to act. Recent research from SenseNova team offers an interesting example of how agent system design can improve embodied AI performance. With GPT-6 Astra's model weights unchanged, SenseNova-RoboRSI reports a RoboDojo average score of 56.83, compared with a published baseline of 28.97, alongside a 50.83% task success rate. It also reports strong results on LIBERO-PRO (94.50%) and RoboCasa (64.60). One strategy explored by SenseNova-RoboRSI is multi-point end-effector (EEF) prediction. Rather than predicting and executing one target at a time, it generates short sequences of action targets in a single step, helping improve motion continuity while reducing repeated model calls. Combined with Planning and Feedback Subagents, the Main Agent can break down tasks, execute actions, monitor progress, and adjust subsequent actions based on feedback. More importantly, SenseNova-RoboRSI brings these strategies into a recursive self-improvement (RSI) loop, where execution results help guide improvements to future agent harness configurations. Improving the harness may become just as important as improving the model itself.