SenseNova-RoboRSI 发布：不改模型权重提升具身智能分数

Nelly; @nrqa__原推发布于 2026.10.10

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Same model, nearly double the RoboDojo score. SenseNova-RoboRSI is exploring a new path for improving Embodied AI. Instead of training a larger model, it focuses on making existing models more effective through a self-improving agent harness. ▪️ RoboDojo: 56.83 average score, 50.83% success rate ▪️ LIBERO-PRO: 94.50 ▪️ RoboCasa: 64.60 On RoboDojo, the score jumps from the public GPT-6 Astra baseline of 28.97 to 56.83, a 96.2% relative improvement, without changing the base model's weights. At the heart of SenseNova-RoboRSI is recursive self-improvement at the harness level. It analyzes execution traces, identifies weaknesses, explores alternative strategies, and repeatedly evaluates the results before incorporating successful improvements into future versions of the agent. Rather than updating the model itself, SenseNova-RoboRSI optimizes how it plans, calls tools, executes actions, and learns from physical feedback. Excited to see where this next step in Embodied AI takes us! SenseNova-RoboRSI GitHub repository is live, with the full code and technical report coming soon: http://github.com/OpenSenseNova/SenseNova-RoboRSI

这条是引用着另一条写的：x.com/sensetime_ai/status/2108929659912221058