As robots make their way out of factories to work alongside humans, it is crucial that they develop the necessary skills to manipulate and interact with their environment in various and unforeseeable circumstances. Pushing, with this regards, becomes a key primitive manipulation skill for a robot's repertoire. Imagine a humanoid robot designate to assist elders in their houses, tasked to fetch a medicine from the top shelf. Instead of picking up each object that obstructs the

I am glad to announce that our paper "Let's Push Things Forward: A Survey on Robot Pushing" has been accepted for publication by Frontiers in Robotics & AI journal. Follow the link below for the paper abstract https://www.frontiersin.org/articles/10.3389/frobt.2020.00008/abstract #frontiers #robot #pushing #survey #deeplearning #analytical #datadriven #frontiers #pushing #robot #survey #deeplearning #analytical #datadriven

Our paper entitled 'Feature-Based Transfer Learning for Robotic Push Manipulation' published in 2018 IEEE International Conference on Robotics and Automation (ICRA) has been selected for an extended version by the Special Issue on Advancement in Engineering and Computer Science organized by Advances in Science, Technology and Engineering Systems Journal (ASTESJ) Thanks to my students, Rhys Howard and Jochen Stuber, the extended paper also proposed a improvement of a twofold i

As robots make their way into our homes, the ability to predict how their actions affect the environment is a key skill that they need to possess to operate in safety and gain our trust. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. This paper is concerned with the evolution of forwards models (FMs) and their applica

This work presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends previous work on modular contact-based predictors by leveraging information on the pushed object's local surface features. We test the hypothesis that, by conditioning predictions on local surface features, we can achieve generalisation across objects of different shapes. Our approach involves learning motion models that are specific to con

Pushing operations are encountered frequently in robotics, but have received comparatively little attention in the research comunity. In one sense, pushing is perhaps the most primitive kind of manipulation, but the relationship between applied pushes and the resulting workpiece motions are complex and hard to predict and control. Push contacts are also important to more complex tasks such as grasping. This work presents an algorithm for planning sequences of pushes, by which