Understanding the mechanisms underlying robust learning and efficient adaptation is an open problem both in neuroscience and machine learning. While robustness and domain adaptation in ML is commonly studied with computer vision tasks, adaptation research in neuroscience has been traditionally carried out in sensorimotor paradigms. My PhD project is a collaborative project between the Mathis lab who studies motor adaptation and the Bethge lab who studies robust ML methods under domain shifts. We will build computational models of neural activity and behavior to closer study neural mechanisms of continual learning during motor adaptation using tools from machine learning. We will compare representations arising in adaptive ML models with those present in the brain. Motor adaptation tasks offer the unique opportunity to precisely control distribution shift, difficulty and learning objectives and link the biological findings to machine learning using insights from dynamical systems and network theory.