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[2607.15275] RoboTTT: Context Scaling for Robot Policies

arxiv.org · 6,971 words · saved by 1 readers

Abstract:Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first time, steady gains in closed-loop performance as pretraining context length scales. At its core, RoboTTT integrates Test-Time Training into robot foundation models such as Vision-Language-Action policies, yielding a sequence model whose recurrent state consists of fast weights, parameters updated by gradient descent during both training and inference, compressing histories into weight space and retrieving contextual information for long-context conditioning. To scale training context length, the recipe combines sequence action forcing with truncated backpropagation through time. On challenging real-robot manipulation tasks, RoboTTT improves overall performance by 87% over the single-step context baseline and fully completes a five-minute, ten-stage assembly task, which no baseline ever does. RoboTTT trained with 8K-timestep context outperforms the same model pretrained with 1K timesteps by 62%, suggesting context length as a new scaling axis for robot foundation models. Videos are available at this https URL

2026-7-17 RoboTTT: Context Scaling for Robot Policies Yunfan Jiang1,2 , Yevgen Chebotar1 , Ruijie Zheng1 , Fengyuan Hu1 , Yunhao Ge1 Jimmy Wu1 , Tianyuan Dai1,3 , Scott Reed1 , Li Fei-Fei2,† , Yuke Zhu1,3,† , Linxi “Jim” Fan1,† 1 NVIDIA 2 Stanford University 3 The University of Texas at Austin † Equal advising research.nvidia.com/labs/gear/robottt…

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