Foundation Models

Autoregressive Policy

An autoregressive policy is a robot policy that generates its output as a sequence of discrete tokens, each conditioned on previously generated tokens, exactly as a language model generates text. Actions are discretized via binning or a learned tokenizer, and the policy factorizes the action distribution across dimensions and timesteps; RT-1, RT-2, and OpenVLA are prominent examples. Sequential decoding limits inference speed, motivating parallel-decoding and diffusion-based alternatives.

Why it matters for physical AI

Casting control as next-token prediction lets robot policies inherit the architectures, pretraining, and scaling behavior of large language models, though decoding latency remains the key obstacle to high-rate control.