A 2-Stage Framework for Learning to Push Unknown Objects

Abstract

Robotic manipulation has been generally applied to particular settings and a limited number of known objects. In order to manipulate novel objects, robots need to be capable of discovering the physical properties of objects, such as the center of mass, and reorienting objects to the desired pose required for subsequent actions. In this work, we proposed a computationally efficient 2-stage framework for planar pushing, allowing a robot to push novel objects to a specified pose with a small amount of pushing steps. We developed three modules: Coarse Action Predictor (CAP), Forward Dynamic Estimator (FDE), and Physical Property Estimator (PPE). The CAP module predicts a mixture of Gaussian distribution of actions. FDE learns the causality between action and successive object state. PPE based on Recurrent Neural Network predicts the physical center of mass (PCOM) from the robot-object interaction. Our preliminary experiments show promising results to meet the practical application requirements of manipulating novel objects.