Abstract:

Robust dexterity depends on the ability to cause and reason about deformation. Robots may routinely need to interact with cables, dough, textiles, and hair in our homes, where deformation is the object state. Soft robots, in turn, are built from compliant materials in which deformation is not a by-product of interaction but its primary mechanism. In this talk, I will discuss mechanics-aware approaches to contact-rich dexterous manipulation with soft objects and soft robots, using mechanics-based priors as a physically grounded intermediate layer between raw sensing and task-level control. These priors take the form of mesh structures with geometric regularization, mechanics modeling, and volumetric dynamics models learned from simulation. I will show how these priors enable robots to perceive deformation through occlusion, predict it, and transfer skills across soft objects, soft robot bodies, and human demonstrations. I will conclude by discussing future directions for mechanics-aware models and robot skill learning frameworks that may scale to diverse deformable objects and compliant morphologies.

Committee: