We won the Amazon Robotics Challenge 2017! 🏆


Check out the media coverage here, including
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Press / Media Information

If you are interested in featuring our team and robot in media and press please contact Juxi Leitner (j.leitner@roboticvision.org) or Kate Haggman (kate.haggman@qut.edu.au) or Tim Macuga (timothy.macuga@roboticvision.org)!
Here's a basic press kit: Images, ACRV Press Release, Videos (tba)

2017 Amazon Robotics Challenge Videos (Playlist)

Software and Hardware Downloads

Gripper design (ThingiVerse):


Latest Updates and News

Read about the technology and science behind #Cartman

We released 4 tech reports about #Cartman, some of them have been submitted to conferences but are already available as preprints on arxiv:

Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. Douglas Morrison, et al.

Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge. S. Wade-McCue, N. Kelly-Boxall, et al.

Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place. M. McTaggart, et al.

Semantic Segmentation from Limited Training Data. Anton Milan, et al.

7News Brisbane (July, 2017)

Meet the Team (July, 2017)

The team is in a frenzy to get all the final preparations going before leaving to Japan later this month. In the meantime the media team is telling you a bit more about the 27 members of the team. The core consists of undergraduate and PhD students at QUT, Adelaide and ANU. #MeetTheTeam

Meet #CartMan, the Robot (July, 2017)

This year we built our own hardware platform, completely from scratch! More to come! Watch the teardown timelapses in the meantime:

Sponsorship (June, 2017)

In the meantime we have secured some extra sponsorships, our sponsors now include:
Queensland University of Technology and
Amazon Robotics

BootCamp at QUT (Mar 3, 2017)

On Friday the 3rd of March the new members of the Amazon Picking Challenge 2017 team met for a Robotics Bootcamp. During the day, a few speakers from the university staff attended and lectured about different topics, including an introduction to the different software the team will be utilising during the building process, and machine learning. The day ended with a look at the 2016 team’s base code, and a demonstration of Harvey, a capsicum-picking robot currently in development.


We are currently on the lookout for motived students to be involved in a QUT-led team entry for the 2017 Amazon Robotics Challenge. We are specifically interested in students interested in working on state-of-the art computer vision, hardware, machine learning/intelligence, and software integration on our robotic platforms (a Baxter robot, Harvey a mobile platform and a currently being built Cartesian robot). Here's a YouTube playlist of last year's team, which came 6th: https://www.youtube.com/playlist?list=PLOqMcGR_zUyEjTO6hE7wM2VpkKGSvRiFg
Requirements are experience with either C++ or Python and Linux. Any prior work with ROS is highly desirable but not a requirement. As this is a competition with a deadline of end of July this year, we are looking for motivated students that are willing to put in the extra work until then. Although this will mean a lot of hours on campus during this time, the second half of your year will be more relaxed and may only need to be report writing (something to be discussed with supervisor). Further, the competition is being held in Japan at the end of July and team members (not all but some) will get an awesome experience and trip to RoboCup to compete live!
Team ACRV (which also involves researchers from Adelaide in addition to QUT) has been selected as one of 16 teams worldwide to again take part this year. We are looking for students to start on this ASAP so if you are keen, please email Juxi Leitner (j.leitner@qut.edu.au) from the QUT Robotics and Autonomous Systems Discipline with your name and student number and prior experience so that we can organise a time to meet.
Media Coverage from last year's competition: BBC, ZDnet


australian centre for robotic vision

Amazon Robotics



2016 Highlights Video

Contact Information


Juxi Leitner

Queensland University of Technology (QUT)
Team Leader