Robot Learning

FiLM Conditioning

FiLM (Feature-wise Linear Modulation) conditioning is a neural network technique, introduced by Perez et al. (2018), in which a conditioning signal generates per-channel scale and shift parameters that modulate intermediate feature maps of another network. In robotics it is widely used to inject language embeddings or goal information into visual encoders, as in BC-Z and RT-1, giving a cheap, effective way to make a single policy task-conditional.

Why it matters for physical AI

Multi-task robot policies need their perception to be steered by instructions; lightweight conditioning mechanisms like FiLM let one network serve many tasks without duplicating encoders or retraining per skill.