Generalizing Task Parameters Through Modularization

We address the problem of generalizing manipulative actions across different tasks and objects. Our robotic agent acquires task-oriented skills from a teacher, and it abstracts skill parameters away from the specificity of the objects and tools used by the teacher. This process enables the transfer of skills to novel objects. Our method relies on the modularization of a task’s representation. Through modularization, we associate each action parameter to a narrow visual modality, therefore facilitating transfers across different objects or tasks. We present a simple experiment where the robot transfers task parameters across three tasks and three objects.