Robotic object manipulation

An important aspect of a robotic system is that it can interact and change the physical world. While seeing is an important sense, picking up objects is arguable one of the most important skills for robots. Object manipulation research involves building novel hardware, designing flexible software and creating fully integrated systems. A big aspect of this research is machine learning and how to create robotic systems that are robust to changes in its environment.

QUT robotics projects consistently feature interdisciplinary teams working alongside industry partners to advance technology and design. The outcomes of the robotic object manipulation projects continue to establish new benchmarks for industry and robotics as a field of research.

We welcome industry collaborations in this field. For more information about collaborating with our team, please contact us.


Amazon Picking Challenge (2017)

The Amazon Robotics Challenge is yearly competition to make an autonomous warehouse robot. QUT's robotics team created a cartesian manipulator dubbed ‘Cartman’, moves along three axes, like a gantry crane, with a rotating gripper to allow item pick-up using either suction or a simple two-finger grip. The team came first in the global competition.

Amazon Picking Challenge (2016)

At the 2016 Amazon Picking Challenge in Germany, 16 teams from around the world competed. The Australian Centre for Robotic Vision (ACRV), headquartered at QUT, reached sixth place in the picking task using a Rethink Robotics Baxter.

ACRV Picking Benchmark

There is an ongoing issue in research fields known as the reproducibility issue. Here, QUT researchers are fostering an important bench-marking system for robot pick and place research.

Deep Learning for Grasping and Manipulation

Deep learning has taken the research world by storm. At QUT, researchers are using advanced deep learning techniques in combination with established approaches to solve new problems.