Robot Learning

Transformer Policy

A transformer policy is a robot control policy implemented as a transformer that maps tokenized observation histories, and often language instructions, to actions. Landmark examples include RT-1, which tokenized camera images and discretized actions for real kitchen tasks, ACT, which decodes action chunks for fine bimanual manipulation, and the autoregressive backbone of VLA models like RT-2 and OpenVLA. Attention over history provides context that reactive feedforward policies lack.

Why it matters for physical AI

Transformer policies scale gracefully with data and model size and integrate naturally with pretrained vision-language backbones, which is why they have become the default architecture for imitation-learned manipulation at every scale.