Review: Three paths for embedding physical priors into robot learning, from classical mechanics to VLA and WAM

The article reviews three paths for embedding physical priors into robot learning: data and representation, model architecture, and training loss, and emphasizes that where a prior is placed determines its constraining effect. Using grasping a soft object as an example, it explains the layered roles of kinematics, dynamics, contact, and material properties in the task, while noting that physical priors are not a panacea and need to match the task.