Peter Corke

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Distinguished Professor of Robotic Vision at QUT; Director Australian Centre for Robotic Vision

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...
01/05/2021 ∙ by Quazi Marufur Rahman, et al. ∙ 0 ∙ share

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Semantics for Robotic Mapping, Perception and Interaction: A Survey

For robots to navigate and interact more richly with the world around th...
01/02/2021 ∙ by Sourav Garg, et al. ∙ 0 ∙ share

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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...
10/17/2020 ∙ by Jesse Haviland, et al. ∙ 0 ∙ share

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

We present NEO, a fast and purely reactive motion controller for manipul...
10/17/2020 ∙ by Jesse Haviland, et al. ∙ 0 ∙ share

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

Manipulating deformable objects, such as fabric, is a long standing prob...
10/07/2020 ∙ by Robert Lee, et al. ∙ 0 ∙ share

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

We present an approach for safe and object-independent human-to-robot ha...
06/02/2020 ∙ by Patrick Rosenberger, et al. ∙ 0 ∙ share

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

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over...
03/03/2020 ∙ by Douglas Morrison, et al. ∙ 0 ∙ share

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Maximising Manipulability During Resolved-Rate Motion Control

Resolved-rate motion control of redundant serial-link manipulators is co...
02/27/2020 ∙ by Jesse Haviland, et al. ∙ 0 ∙ share

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Robot Navigation in Unseen Spaces using an Abstract Map

Human navigation in built environments depends on symbolic spatial infor...
01/31/2020 ∙ by Ben Talbot, et al. ∙ 6 ∙ share

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Model-free vision-based shaping of deformable plastic materials

We address the problem of shaping deformable plastic materials using non...
01/30/2020 ∙ by Andrea Cherubini, et al. ∙ 0 ∙ share

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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...
01/16/2020 ∙ by Jesse Haviland, et al. ∙ 6 ∙ share

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What can robotics research learn from computer vision research?

The computer vision and robotics research communities are each strong. H...
01/08/2020 ∙ by Peter Corke, et al. ∙ 50 ∙ share

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

We propose a new visual object detector evaluation measure which not onl...
11/27/2018 ∙ by David Hall, et al. ∙ 0 ∙ share

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

Camera viewpoint selection is an important aspect of visual grasp detect...
09/23/2018 ∙ by Douglas Morrison, et al. ∙ 0 ∙ share

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

Robots must reliably interact with refractive objects in many applicatio...
05/31/2018 ∙ by Dorian Tsai, et al. ∙ 0 ∙ share

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The Limits and Potentials of Deep Learning for Robotics

The application of deep learning in robotics leads to very specific prob...
04/18/2018 ∙ by Niko Sünderhauf, et al. ∙ 0 ∙ share

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

This paper presents a real-time, object-independent grasp synthesis meth...
04/14/2018 ∙ by Douglas Morrison, et al. ∙ 0 ∙ share

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

This paper presents the design and implementation of an assisted control...
04/06/2018 ∙ by Aaron McFadyen, et al. ∙ 0 ∙ share

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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 ...
09/18/2017 ∙ by Fangyi Zhang, et al. ∙ 0 ∙ share

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Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision,...
05/24/2017 ∙ by Quentin Bateux, et al. ∙ 0 ∙ share

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Episode-Based Active Learning with Bayesian Neural Networks

We investigate different strategies for active learning with Bayesian de...
03/21/2017 ∙ by Feras Dayoub, et al. ∙ 0 ∙ share

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Mirrored Light Field Video Camera Adapter

This paper proposes the design of a custom mirror-based light field came...
12/16/2016 ∙ by Dorian Tsai, et al. ∙ 0 ∙ share

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

While deep learning has had significant successes in computer vision tha...
10/21/2016 ∙ by Fangyi Zhang, et al. ∙ 0 ∙ share

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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 ...
09/17/2016 ∙ by Jürgen Leitner, et al. ∙ 0 ∙ share

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

Fine-grained classification is a relatively new field that has concentra...
08/01/2016 ∙ by ZongYuan Ge, et al. ∙ 0 ∙ share

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Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

We present a novel deep convolutional neural network (DCNN) system for f...
11/30/2015 ∙ by ZongYuan Ge, et al. ∙ 0 ∙ share

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Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control

This paper introduces a machine learning based system for controlling a ...
11/12/2015 ∙ by Fangyi Zhang, et al. ∙ 0 ∙ share

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Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small ...
05/09/2015 ∙ by ZongYuan Ge, et al. ∙ 0 ∙ share

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

We propose a local modelling approach using deep convolutional neural ne...
02/27/2015 ∙ by ZongYuan Ge, et al. ∙ 0 ∙ share

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Peter Corke

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