211 citations · 432 across the 31 of their papers we have counts for
8 papers · 1 filter
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, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place
M. McTaggart, D. Morrison, A. W. Tow +8
Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Fangyi Zhang, Jürgen Leitner, Zongyuan Ge +2
Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real…
Visual Servoing from Deep Neural Networks
Quentin Bateux, Eric Marchand, Jürgen Leitner +2
We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various…