b'Peter Corke'

research

∙ 04/29/2022

Eyes on the Prize: Improved Perception for Robust Dynamic Grasping

This paper is concerned with perception challenges for robust grasping i...

Ben Burgess-Limerick, et al. ∙

research

∙ 02/25/2022

Visibility Maximization Controller for Robotic Manipulation

Occlusions caused by a robot's own body is a common problem for closed-l...

Kerry He, et al. ∙

research

∙ 09/10/2021

A Holistic Approach to Reactive Mobile Manipulation

We present the design and implementation of a taskable reactive mobile m...

Jesse Haviland, et al. ∙

research

∙ 08/19/2021

FSNet: A Failure Detection Framework for Semantic Segmentation

Semantic segmentation is an important task that helps autonomous vehicle...

Quazi Marufur Rahman, et al. ∙

research

∙ 04/15/2021

Tabletop Object Rearrangement: Team ACRV's Entry to OCRTOC

Open Cloud Robot Table Organization Challenge (OCRTOC) is one of the mos...

Zheyu Zhang, et al. ∙

research

∙ 03/29/2021

Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion

Curved refractive objects are common in the human environment, and have ...

Dorian Tsai, et al. ∙

research

∙ 01/05/2021

Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

As deep learning continues to dominate all state-of-the-art computer vis...

Quazi Marufur Rahman, et al. ∙

research

∙ 01/02/2021

Semantics for Robotic Mapping, Perception and Interaction: A Survey

For robots to navigate and interact more richly with the world around th...

Sourav Garg, et al. ∙

research

∙ 10/17/2020

A Systematic Approach to Computing the Manipulator Jacobian and Hessian using the Elementary Transform Sequence

The elementary transform sequence (ETS) provides a universal method of d...

Jesse Haviland, et al. ∙

research

∙ 10/17/2020

NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipul...

Jesse Haviland, et al. ∙

research

∙ 10/07/2020

Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience

Manipulating deformable objects, such as fabric, is a long standing prob...

Robert Lee, et al. ∙

research

∙ 06/02/2020

Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

We present an approach for safe and object-independent human-to-robot ha...

Patrick Rosenberger, et al. ∙

research

∙ 03/03/2020

EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over...

Douglas Morrison, et al. ∙

research

∙ 02/27/2020

Maximising Manipulability During Resolved-Rate Motion Control

Resolved-rate motion control of redundant serial-link manipulators is co...

Jesse Haviland, et al. ∙

research

∙ 01/31/2020

Robot Navigation in Unseen Spaces using an Abstract Map

Human navigation in built environments depends on symbolic spatial infor...

Ben Talbot, et al. ∙

research

∙ 01/30/2020

Model-free vision-based shaping of deformable plastic materials

We address the problem of shaping deformable plastic materials using non...

Andrea Cherubini, et al. ∙

research

∙ 01/16/2020

Predicting Target Feature Configuration of Non-stationary Objects for Grasping with Image-Based Visual Servoing

In this paper we consider the problem of the final approach stage of clo...

Jesse Haviland, et al. ∙

research

∙ 01/08/2020

What can robotics research learn from computer vision research?

The computer vision and robotics research communities are each strong. H...

Peter Corke, et al. ∙

research

∙ 11/27/2018

Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation

We propose a new visual object detector evaluation measure which not onl...

David Hall, et al. ∙

research

∙ 09/23/2018

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Camera viewpoint selection is an important aspect of visual grasp detect...

Douglas Morrison, et al. ∙

research

∙ 05/31/2018

Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion

Robots must reliably interact with refractive objects in many applicatio...

Dorian Tsai, et al. ∙

research

∙ 04/18/2018

The Limits and Potentials of Deep Learning for Robotics

The application of deep learning in robotics leads to very specific prob...

Niko Sünderhauf, et al. ∙

research

∙ 04/14/2018

Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

This paper presents a real-time, object-independent grasp synthesis meth...

Douglas Morrison, et al. ∙

research

∙ 04/06/2018

Assisted Control for Semi-Autonomous Power Infrastructure Inspection using Aerial Vehicles

This paper presents the design and implementation of an assisted control...

Aaron McFadyen, et al. ∙

research

∙ 09/18/2017

Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks

A modular method is proposed to learn and transfer visuo-motor policies ...

Fangyi Zhang, et al. ∙

research

∙ 05/24/2017

Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision,...

Quentin Bateux, et al. ∙

research

∙ 03/21/2017

Episode-Based Active Learning with Bayesian Neural Networks

We investigate different strategies for active learning with Bayesian de...

Feras Dayoub, et al. ∙

research

∙ 12/16/2016

Mirrored Light Field Video Camera Adapter

This paper proposes the design of a custom mirror-based light field came...

Dorian Tsai, et al. ∙

research

∙ 10/21/2016

Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies

While deep learning has had significant successes in computer vision tha...

Fangyi Zhang, et al. ∙

research

∙ 09/17/2016

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

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA ...

Jürgen Leitner, et al. ∙

research

∙ 08/01/2016

Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

Fine-grained classification is a relatively new field that has concentra...

ZongYuan Ge, et al. ∙

research

∙ 11/30/2015

Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

We present a novel deep convolutional neural network (DCNN) system for f...

ZongYuan Ge, et al. ∙

research

∙ 11/12/2015

Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control

This paper introduces a machine learning based system for controlling a ...

Fangyi Zhang, et al. ∙

research

∙ 05/09/2015

Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small ...

ZongYuan Ge, et al. ∙

research

∙ 02/27/2015

Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

We propose a local modelling approach using deep convolutional neural ne...

ZongYuan Ge, et al. ∙

Peter Corke

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