Object Singulation by Nonlinear Pushing for Robotic Grasping In this study, we aim at grasping a single target object in a cluttered environment using a robotic arm. While dexterous grasp for various shapes of objects is not considered in this work, we focus on developing the method to mitigate clutter near the target object as soon as quickly. For this purpose, we propose a method to generate nonlinear pushing motions for object singulation based on an offtheshelf machine learning algorithm and a typical semantic segmentation algorithm. Through experiments, we show that the success rate of robotic grasping is considerably improved by the proposed pushing behavior. And notably, the nonlinear pushing trajectories allows the robot to perform singulation of the target object in a cluttered environment with fewer trials than linear pushing usually pursued in related works.

Omnipush: accurate, diverse, realworld dataset of pushing dynamics with RGBD video Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, inhand manipulation and clearing clutter. Such models, however, are approximate, which limits their applicability.Learningbased methods can reason directly from raw sensory data with accuracy, and have the potential to generalize to a wider diversity of scenarios. However, developing and testing such methods requires richenough datasets. In this paper we introduce Omnipush, a dataset with high variety of planar pushing behavior.In particular, we provide 250 pushes for each of 250 objects, all recorded with RGBD and a high precision tracking system. The objects are constructed so as to systematically explore key factors that affect pushingxe2x80x93the shape of the object and its mass distributionxe2x80x93which have not been broadly explored in previous datasets, and allow to study generalization in model learning.Omnipush includes a benchmark for metalearning dynamic models, which requires algorithms that make good predictions and estimate their own uncertainty. We also provide an RGB video prediction benchmark and propose other relevant tasks that can be suited with this dataset. Data and code are available at https:web.mit.edumcubeomnipushdataset.