RoboTTT：機器人策略的上下文縮放

摘要

近期的機器人基礎模型僅能處理單步或短歷史的視覺運動上下文。我們引入了測試時訓練機器人策略（RoboTTT），這是一種機器人模型與訓練方法，能將視覺運動上下文擴展至8000時間步，比當前最佳策略高出三個數量級，且不增加推理延遲。在如此上下文長度下，我們解鎖了新的機器人能力：從人類影片示範中進行單次上下文模仿、即時策略改進、對擾動的穩健性，以及在多階段、長時間任務中的更強表現。我們也首次觀察到，隨著預訓練上下文長度擴展，閉環性能持續提升。其核心是，RoboTTT將測試時訓練整合到如視覺-語言-行動策略等機器人基礎模型中，產生一個序列模型，其遞迴狀態由快速權重組成，這些參數在訓練與推理期間皆透過梯度下降更新，將歷史壓縮至權重空間，並檢索上下文資訊以實現長上下文條件化。為了擴展訓練上下文長度，該方法結合了序列動作強制與截斷時間反向傳播。在具挑戰性的真實機器人操作任務中，RoboTTT相較於單步上下文基準提升了87%的整體表現，並完整完成一項五分鐘、十階段的組裝任務，這是任何基準方法從未達到的。以8000時間步上下文訓練的RoboTTT，其表現比相同模型以1000時間步預訓練高出62%，這表明上下文長度可作為機器人基礎模型的一個新擴展軸。影片請見 https://research.nvidia.com/labs/gear/robottt/

English

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/