RoboTTT: Context Scaling for Robot Policies

Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng, Fengyuan Hu, Yunhao Ge, Jimmy Wu, Tianyuan Dai, Scott Reed, Li Fei-Fei, Yuke Zhu, Linxi "Jim" Fan · N/A · 2026

Summary

Recent robot foundation models operate with single-step or short-history visuomotor context.

Abstract Summary

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 https://research.nvidia.com/labs/gear/robottt/

Key Points

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.

Preliminaries

Test-Time Training Mechanism Test-Time Training (TTT) ( 64 ; 78 ) introduces fast weights that are updated during both training and inference to dynamically model contextual information. This contrasts with slow weights (i.e., model parameters), which are updated only during training and remain frozen during inference.

Method: Test-Time-Training Robot Policies

In this section, we introduce Test-Time-Training Robot Policies ( RoboTTT ), a robot model and training recipe for learning over long-context robot trajectories. We first describe the model architecture and how we integrate TTT into modern robot foundation models, then a training recipe that combines sequence action forcing with truncated backpropagation through time (TBPTT) to scale training context length.

Experiments

Experiment Settings We evaluate on three long-horizon, multi-stage assembly tasks requiring bimanual manipulation and dexterity (Fig. 5 ): Pup Go Car, Circuit, and Gear Bot. All experiments use a YAM bimanual setup with four RGB cameras: top, bottom, left wrist, and right wrist.

For each task, we collect 8, 6, and 5 hours of real-robot data, with average episode lengths of 2 minutes, 1 minute, and 5 minutes, respectively.

Experiments — RoboTTT: Context Scaling for Robot Policies — Figure 5: Evaluation tasks. Three long-horizon assembly tasks on a YAM bimanual setup; each row shows one rollout. Top, Pup Go Car: toy vehicle assembly, 2-minute average episodes. Middle, Circuit: target configuration specified by a language prompt or a one-shot human video demonstration; 1-minute episodes. Bottom, Gear Bot: ten-stage assembly spanning five minutes, our longest-horizon task.

Sources and Demonstrations

Figures are reproduced from Jiang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.