Foundation Models
Pretrained Visual Representations
Pretrained visual representations (PVRs) are vision encoders trained on large image or video corpora and reused, frozen or fine-tuned, as the perception backbone of robot policies. Prominent examples include R3M, trained on Ego4D egocentric video with time-contrastive and language-alignment objectives, MVP and VC-1 based on masked autoencoding, and CLIP-style encoders. Benchmarks such as CortexBench show their benefits vary considerably across tasks and evaluation conditions.
Why it matters for physical AI
Reusing encoders pretrained on abundant human video sidesteps the scarcity of robot data for perception, though choosing and adapting the right representation for control remains an empirical, task-dependent decision.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.