
HKU Team's PhysicalRSI 1.0 Tops RoboDojo Benchmark at 1% of GPT-6 Astra's Cost
A research team at the University of Hong Kong’s MMLab has proposed a simple and efficient self-evolving embodied AI baseline, PhysicalRSI 1.0, which ranked first on the RoboDojo benchmark with a 31.38% average task success rate at roughly 1% of the token cost of GPT-6 Astra, the team said. PhysicalRSI 1.0, short for Physical Recursive Self-Improvement, achieved a 36.27 score and 31.38% success rate under a unified five-category evaluation, compared with GPT-6 Astra’s 28.86 score and 22.21% success rate. The result represents a 39.6% relative improvement in success rate over Astra at a fraction of the cost. Astra reportedly consumes about 200,000 yuan in tokens for a full RoboDojo test run. The system uses a “System 1–2 Co-Evolution” framework in...
AGIBOT
Figure AI
UBTECH