About Doug

Doug Morrison

Hi, I'm Doug Morrison. I'm a PhD researcher at the Australian Centre for Robotic Vision (ACRV) at the Queensland University of Technology (QUT) in Brisbane, Australia, supervised by Dr Juxi Leitner and Professor Peter Corke.

My research is developing new strategies for robotic grasping in the unstructured and dynamic environments of the real world, that is, strategies which are general, reactive and knowledgeable about their environments. The goal: create robots that can grasp objects anywhere, all the time.

I was also one of the lead developers of Cartman, the ACRV's winning entry into the 2017 Amazon Robotics Challenge!

News

  • Apr 2018 - My paper "Closing the Loop for Robotic Grasping" was accepted to RSS 2018! [arXiv][Video]
  • Apr 2018 - I discuss robotic grasping and the ARC on NVIDIA's "The AI Podcast" [Listen Here]
  • Mar 2018 - Cartman's packing his bags again and heading to GTC 2018 in Silicon Valley, where Juxi and I will be giving a presentation. [Slides]
  • Jan 2018 - Two papers accepted to ICRA 2018 [arXiv, arXiv]
  • Jul 2017 - We won the Amazon Robotics Challenge 2017! Congratualtions to everyone involved!

Selected Publications

(See All)

Doug Morrison, Peter Corke, Juxi Leitner
Robotics: Science and Systems (RSS), 2018
This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. The lightweight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies.
Doug Morrison, AW Tow, M McTaggart, R Smith, N Kelly-Boxall, S Wade-McCue, J Erskine, R Grinover, A Gurman, T Hunn, D Lee, A Milan, T Pham, G Rallos, A Razjigaev, T Rowntree, K Vijay, Z Zhuang, C Lehnert, I Reid, P Corke, J Leitner
International Conference on Robotics and Automation (ICRA), 2018
A system-level description of Cartman, our winning entry into the 2017 Amazon Robotics Challenge.
A. Milan, T. Pham, K. Vijay, D. Morrison, A.W. Tow, L. Liu, J. Erskine, R. Grinover, A. Gurman, T. Hunn, N. Kelly-Boxall, D. Lee, M. McTaggart, G. Rallos, A. Razjigaev, T. Rowntree, T. Shen, R. Smith, S. Wade-McCue, Z. Zhuang, C. Lehnert, G. Lin, I. Reid, P. Corke, J. Leitner
International Conference on Robotics and Automation (ICRA), 2018
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored.