Nvidia stretches robot memory to 8,000 steps with RoboTTT
Nvidia and Stanford researchers just unveiled RoboTTT, scaling a robot's visuomotor context to 8,000 timesteps—roughly five minutes of continuous real-time control. By using Test-Time Training to compress action history into fast weights without adding inference latency, the robot aced a 10-stage assembly task where traditional baselines failed. Why it matters: Context length is emerging as the next big scaling law for physical AI.