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 perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first
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- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- LinkedLinked via arxiv author · 85%Yunfan Jiang →
“RoboTTT: Context Scaling for Robot Policies”
- LinkedLinked via arxiv author · 85%Yevgen Chebotar →
“RoboTTT: Context Scaling for Robot Policies”
- LinkedLinked via arxiv author · 85%Ruijie Zheng →
“RoboTTT: Context Scaling for Robot Policies”
- LinkedLinked via arxiv author · 85%Fengyuan Hu →
“RoboTTT: Context Scaling for Robot Policies”
- LinkedLinked via arxiv author · 85%Yunhao Ge →
“RoboTTT: Context Scaling for Robot Policies”
- LinkedLinked via arxiv author · 85%Jimmy Wu →
“RoboTTT: Context Scaling for Robot Policies”