RoboTTT

RoboTTT scales robot policy context to 8K timesteps via Test-Time Training, enabling one-shot imitation and long-horizon tasks

NVIDIA researchers introduce RoboTTT, a robot foundation model training recipe that extends visuomotor context to 8,000 timesteps — three orders of magnitude beyond current state-of-the-art — without increasing inference latency. The approach integrates Test-Time Training into Vision-Language-Action policies, using fast weights (parameters updated by gradient descent during inference) to compress long histories into weight space. On real-robot manipulation tasks, RoboTTT achieves 87% performance improvement over single-step baselines and is the first system to fully complete a five-minute, ten-stage assembly task. The work identifies context length as a new scaling axis for robot foundation models, with 8K-context pretraining outperforming 1K-context by 62%.