Learning Object Models For Non-prehensile Manipulation

Abstract

Using models to represent information about the world is a well known paradigm for successful robot control in the real world. Numerous methods exist today that can leverage these models to make robots perform tasks, either by directly exploiting the model structure or by accessing the model via a simulation. In this work, we explore how robots can use demonstrations to quickly build descriptive models of objects for manipulation tasks. Our framework uses demonstrations to incrementally build task-relevant geometric and physics-based object models that can be used to build simulations of the world that the robot is interacting with. We present experiments that involve estimating geometric features of an object when demonstration data from a user interacting with the object is available. We also demonstrate our method on the task of toppling a box with a 7-DoF manipulator equipped with a palm at its end. Using our approach, the robot is able to complete the task using only a few demonstrations.