Astra agent performs zero-shot robot control with 95% success rate in real-world tests

OpenAI’s GPT-6 Astra has crossed a threshold that robotics researchers have been chasing for years: controlling a full humanoid robot to perform pick-and-place tasks without any task-specific training. The model accomplished this using zero-shot control, meaning it figured out how to manipulate the robot’s body purely from its general training, no fine-tuning required. What Astra actually did In evaluations conducted by third-party organization RoboCurve, Astra achieved a 95% success rate on a physical pick-and-place task, successfully completing 19 out of 20 attempts using dual bimanual robot arms known as the I2RT YAM. Each run consumed roughly 2.1k tokens of output and finished in approximately 2.5 minutes. In simulation environments, the numbers got even better. Astra hit a 98% success rate, nailing 49 out of 50 single-arm assignments in RoboLab benchmarks. The model also pulled off a zero-shot cola-bottle pickup in simulated humanoid control, translating human demonstrations into robot actions using only visual inputs from cameras. No privileged access to the robot’s internal state data. No specialized robotic datasets. Just observation-to-action loops integrating camera views...