most citedAutonomous Sweet Pepper Harvesting for Protected Cropping Systems

266 citations · 322 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2017★ 17 cited

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…

cs.RO2017★ 10 cited

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…

cs.RO2017★ 5 cited

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…

cs.RO2017★ 15 cited

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…

cs.RO2017★ 266 cited

Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

Chris Lehnert, Andrew English, Chris McCool +2

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision al…

cs.RO2017★ 4 cited

What Would You Do? Acting by Learning to Predict

Adam Tow, Niko Sünderhauf, Sareh Shirazi +2

We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perfo…

cs.RO2016★ 5 cited

The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research

Jürgen Leitner, Adam W. Tow, Jake E. Dean +15

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable…