Modeling Dynamic Hand-Object Interactions with Applications to Human-Robot Handovers

Humans frequently grasp, manipulate, and move objects. Interactive systems assist humans in these tasks, enabling applications in Embodied AI, human-robot interaction, and virtual reality. However, current methods in hand-object synthesis often neglect dynamics and focus on generating static grasps. The first part of this dissertation introduces dynamic grasp synthesis, where a hand grasps and moves an object to a target pose. We approach this task using physical simulation and reinforcement learning. We then extend this to bimanual manipulation and articulated objects, requiring fine-grained coordination between hands. In the second part of this dissertation, we study human-to-robot handovers. We integrate captured human motion into simulation and introduce a student-teacher framework that adapts to human behavior and transfers from sim to real. To overcome data scarcity, we generate synthetic interactions, increasing training diversity by 100x. Our user study finds no difference between policies trained on synthetic vs. real motions.