Robot Learning

Domain Adaptation

Domain Adaptation is a set of transfer learning techniques for making a model trained on a source data distribution perform well on a different target distribution, typically with little or no labeled target data. Approaches include adversarial feature alignment, image-to-image translation such as CycleGAN-based sim-to-real pixel adaptation, and self-training on pseudo-labels. In robotics the canonical source-target pairs are simulation versus reality and one deployment site versus another.

Why it matters for physical AI

Every new cell, warehouse, or home shifts lighting, textures, and object distributions away from the training set. Adaptation techniques reduce the recollection and retraining cost each shift would otherwise incur.