266 citations · 470 across the 18 of their papers we have counts for
20 papers
Brace Yourself: Task-Conditioned Environmental Bracing for Forceful Humanoid Manipulation
Zongyuan Zhang, Christopher Lehnert, Will N. Browne +1
Forceful manipulation is challenging for humanoid robots because interaction forces can disturb whole-body balance. We introduce the Supporting Hand Strategy (SHS), which enables a…
Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments
Ben Burgess-Limerick, Jesse Haviland, Chris Lehnert +1
We present a reactive base control method that enables high performance mobile manipulation on-the-move in environments with static and dynamic obstacles. Performing manipulation t…
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
Nicolas Harvey Chapman, Feras Dayoub, Will Browne +1
Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to…
An Architecture for Reactive Mobile Manipulation On-The-Move
Ben Burgess-Limerick, Chris Lehnert, Jurgen Leitner +1
We present a generalised architecture for reactive mobile manipulation while a robot's base is in motion toward the next objective in a high-level task. By performing tasks on-the-…
Developing cooperative policies for multi-stage reinforcement learning tasks
Jordan Erskine, Chris Lehnert
Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider…
DGBench: An Open-Source, Reproducible Benchmark for Dynamic Grasping
Ben Burgess-Limerick, Chris Lehnert, Jurgen Leitner +1
This paper introduces DGBench, a fully reproducible open-source testing system to enable benchmarking of dynamic grasping in environments with unpredictable relative motion between…
Combining Local and Global Viewpoint Planning for Fruit Coverage
Tobias Zaenker, Chris Lehnert, Chris McCool +1
Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially…
Developing cooperative policies for multi-stage tasks
Jordan Erskine, Chris Lehnert
This paper proposes the Cooperative Soft Actor Critic (CSAC) method of enabling consecutive reinforcement learning agents to cooperatively solve a long time horizon multi-stage tas…
Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments
Paul Zapotezny-Anderson, Chris Lehnert
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presen…
A Sweet Pepper Harvesting Robot for Protected Cropping Environments
Chris Lehnert, Chris McCool, Inkyu Sa +1
Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this p…
3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation
Chris Lehnert, Dorian Tsai, Anders Eriksson +1
In this paper, we present a new approach to visual servoing for robotics, referred to as 3D Move to See (3DMTS), based on the principle of finding the next best view using a 3D cam…
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…
In-Field Peduncle Detection of Sweet Peppers for Robotic Harvesting: a comparative study
Chris Lehnert, Chris McCool, Tristan Perez
Robotic harvesting of crops has the potential to disrupt current agricultural practices. A key element to enabling robotic harvesting is to safely remove the crop from the plant wh…
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…
Lessons Learnt from Field Trials of a Robotic Sweet Pepper Harvester
Christopher Lehnert, Christopher McCool, Tristan Perez
In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping…
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…
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information
Inkyu Sa, Chris Lehnert, Andrew English +4
This paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is on…
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…