Perceptron AI Launches Isaac 0.5, a Frontier Open-Weight Robotics Model
Perceptron’s Isaac 0.5 outperforms leading open robot models, including Physical Intelligence's π0.5 and NVIDIA’s GR00T N1.7
Perceptron AI has launched Isaac 0.5, a 36-billion-parameter open-weight embodied foundation model that combines video understanding, embodied reasoning and robot control, the first open model at the frontier of all three. Perceptron is working with customers to adapt the model for industrial systems.
Isaac can read video, follow language instructions, locate and track objects, estimate the state of a task, and generate robot actions. Industrial automation and robotics teams can use it as the policy that controls a robot or use its visual outputs inside an existing planning and control system.
Isaac was trained on three trillion multimodal tokens, one million hours of general video and 100,000 hours of robotics-oriented experience across more than 35 robot systems. Perceptron also established a new scaling law for the data behind robot models. In controlled training experiments, scaling general video from 1,000 hours to one million cut the teleoperation needed to reach the same, well-calibrated action loss from ~5,900 hours to 28.
