RoboTTT: Context Scaling for Robot Policies

Key points

It integrates Test-Time Training, fast weights, into a VLA model to achieve an 8K time-step context without increasing inference latency.
With just one human video demonstration, it gains one-shot imitation of a new behavior, succeeding in 6 out of 10 cases while all baselines fail.
DAgger Distillation enables real-time performance improvement, reaching 83% success under disturbances, compared with a previous best of 53%.
The overall task success rate improves by 87% over the single-step baseline, and it completes a 5-minute, 10-step assembly all the way through, which no previous model could do.
Expanding context length from 1K to 8K yields 62% better performance, showing that context is a scaling axis for robots as well.

External research summaries. These are not HDATF publications or measured product results.