  Advances in artificial intelligence (AI) have been propelling the evolution
of human-robot interaction (HRI) technologies. However, significant challenges
remain in achieving seamless interactions, particularly in tasks requiring
physical contact with humans. These challenges arise from the need for accurate
real-time perception of human actions, adaptive control algorithms for robots,
and the effective coordination between human and robotic movements. In this
paper, we propose an approach to enhancing physical HRI with a focus on dynamic
robot-assisted hand-object interaction (HOI). Our methodology integrates hand
pose estimation, adaptive robot control, and motion primitives to facilitate
human-robot collaboration. Specifically, we employ a transformer-based
algorithm to perform real-time 3D modeling of human hands from single RGB
images, based on which a motion primitives model (MPM) is designed to translate
human hand motions into robotic actions. The robot's action implementation is
dynamically fine-tuned using the continuously updated 3D hand models.
Experimental validations, including a ring-wearing task, demonstrate the
system's effectiveness in adapting to real-time movements and assisting in
precise task executions.
