RoboTTT: Context Scaling for Robot Policies

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 perturb

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ethics.ai (16 July 2026), “RoboTTT: Context Scaling for Robot Policies,” evidence record 11587, https://ethics.ai/record/11587 (originally published by arXiv cs.AI).

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