Foundation Models

Perceiver

Perceiver is a transformer architecture introduced by DeepMind in 2021 that scales to very high-dimensional inputs by cross-attending from a small set of learned latent vectors to the raw input array, decoupling compute from input size. Its successor, Perceiver IO, added flexible structured outputs. In robotics, PerAct used a Perceiver backbone to map voxelized RGB-D observations and language goals to 6-DoF manipulation actions.

Why it matters for physical AI

Robot observations mix cameras, depth, proprioception, and language at very different resolutions; latent-bottleneck architectures like Perceiver offer a clean way to fuse such heterogeneous inputs without quadratic attention cost.