Robot Learning

Neural Process

A neural process is a model, introduced by Garnelo et al. in 2018, that learns distributions over functions by encoding a set of context observations into a latent representation and predicting outputs, with uncertainty, at new inputs. Neural processes combine the flexibility of neural networks with Gaussian-process-like uncertainty estimates and adapt to new tasks from small context sets without gradient updates.

Why it matters for physical AI

Fast adaptation from a handful of examples with calibrated uncertainty is valuable for meta-learning dynamics models and for robots that must adjust to new objects or conditions on the fly.