The presentation of our research paper at the 2012 IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
The paper "Transferring Spatial Perception Between Robots Operating In A Shared Workspace" can be found at http://Juxi.net/papers
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Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
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Panel Discussion: Outlook for the Precious Metals Markets
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Bill Fisher - RX Exploration
David Hargreaves - Fair Trade Gemstones
Economics of Cocoa,Profitability of Cocoa Farming in West Africa,What is the greatest threat to the cocoa supply?The R4D Context,Sustainable Tree Crops Development Alliance and Program,Synergies of the Partnership
The Need For Robots To Grasp the WorldJuxi Leitner
These slides were used for a few talks in the last couple of months to excite people about the intelligent robotic systems. In particular, why I believe that it is important for robots to grasp the world, both in the sense of perceiving and understanding but also in the physical sense of actually changing the state of the world by picking objects and interacting with a wide range of items.
These slides (with slight variations) were presented at QUT, Uni Sydney, Uni Cambridge, DeepMind, Uni Birmingham, Amazon Robotics, ...
Significant progress in computer vision in the past years has excited a whole field of researchers. In robotics we are now able to use these techniques to build robotic systems that can observe, understand, and interact with the world, in short, we can build robots that grasp the world.
This is an overview of the efforts in the Australien Centre for Robotic Vision under the umbrella of "Robotic Manipulation" led by Dr. Juxi Leitner.
Slides used for a series of presentations in Australia and Europe in Sep/Oct 2018.
Feel free to reach out for opportunities to juxi@lyro.io
Improving Robotic Manipulation with Vision and Learning @AmazonDevCentre BerlinJuxi Leitner
My talk at the Amazon Development Centre in Berlin. Including work on how to improve robotic reaching, grasping and manipulation. And getting away from chasing grasp success rates.
Cartman, how to win the amazon robotics challenge with robotic vision and dee...Juxi Leitner
Cartman, how to win the amazon robotics challenge with robotic vision and deep learning #GTC18 S8842
Douglas Morrison and Juxi Leitner
Australian Centre for Robotic Vision
roboticvision.org
These slides were used in the guest lecture for QUT's Image processing class.
The two part presentation consists of our Amazon Robotics Challenge robot #Cartman and some introduction to (deep) reinforcement learning.
ACRV Picking Benchmark: how to benchmark pick and place robotics researchJuxi Leitner
Presented at the IROS workshop on "DEVELOPMENT OF BENCHMARKING PROTOCOLS FOR ROBOTIC MANIPULATION"
http://ycbbenchmarks.org/IROS2017workshop.html
The ACRV Picking Benchmark has been developed over the last year to facilitate comparison of robotic systems in pick and place settings!
With the ABP we propose a physical benchmark for robotic picking: overall design, objects, configuration, and guidance on appropriate technologies to solve it. Challenges are an important way to drive progress but they occur only occasionally and the test conditions are difficult to replicate outside the challenge. This benchmark is motivated by experience in the recent Amazon Picking Challenge and contains a commonly-available shelf, 42 objects, a set of stencils and standardized task setups.
A major focus through the design of this benchmark was to maximise reproducibility: a number of carefully chosen scenarios with precise instructions on how to place, orient, and align objects with the help of printable stencils are defined. To make the benchmark as accessible as possible to the research community, a white IKEA shelf is used for all picking tasks. Furthermore, we carefully curated a set of 42 objects to ensure global availability and reduced chance of import restrictions.
Team ACRV's experience at #AmazonPickingChallenge 2016Juxi Leitner
Building on Repeatable Grasping Experiments
Team ACRV: Lessons Learned from the Amazon Picking Challenge 2016
Juxi Leitner, ACRV, Queensland University of Technology (Team ACRV, 2016, 2017)
We describe our entry into the 2016 Amazon Picking Challenge (APC) and the lessons learned from deploying a complex, robotic system outside of the lab. To help future developments decided to create a new physical benchmark challenge for robotic picking to drive scientific progress and make research into (end-to-end) picking comparable. It consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils.
A guest lecture about (deep) reinforcement learning and on-going projects includign at QUT. This is for the Machine Learningcourse (CAB 420) at the Queensland University of Technology (QUT)
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The Need For Robots To Grasp the WorldJuxi Leitner
These slides were used for a few talks in the last couple of months to excite people about the intelligent robotic systems. In particular, why I believe that it is important for robots to grasp the world, both in the sense of perceiving and understanding but also in the physical sense of actually changing the state of the world by picking objects and interacting with a wide range of items.
These slides (with slight variations) were presented at QUT, Uni Sydney, Uni Cambridge, DeepMind, Uni Birmingham, Amazon Robotics, ...
Significant progress in computer vision in the past years has excited a whole field of researchers. In robotics we are now able to use these techniques to build robotic systems that can observe, understand, and interact with the world, in short, we can build robots that grasp the world.
This is an overview of the efforts in the Australien Centre for Robotic Vision under the umbrella of "Robotic Manipulation" led by Dr. Juxi Leitner.
Slides used for a series of presentations in Australia and Europe in Sep/Oct 2018.
Feel free to reach out for opportunities to juxi@lyro.io
Improving Robotic Manipulation with Vision and Learning @AmazonDevCentre BerlinJuxi Leitner
My talk at the Amazon Development Centre in Berlin. Including work on how to improve robotic reaching, grasping and manipulation. And getting away from chasing grasp success rates.
Cartman, how to win the amazon robotics challenge with robotic vision and dee...Juxi Leitner
Cartman, how to win the amazon robotics challenge with robotic vision and deep learning #GTC18 S8842
Douglas Morrison and Juxi Leitner
Australian Centre for Robotic Vision
roboticvision.org
These slides were used in the guest lecture for QUT's Image processing class.
The two part presentation consists of our Amazon Robotics Challenge robot #Cartman and some introduction to (deep) reinforcement learning.
ACRV Picking Benchmark: how to benchmark pick and place robotics researchJuxi Leitner
Presented at the IROS workshop on "DEVELOPMENT OF BENCHMARKING PROTOCOLS FOR ROBOTIC MANIPULATION"
http://ycbbenchmarks.org/IROS2017workshop.html
The ACRV Picking Benchmark has been developed over the last year to facilitate comparison of robotic systems in pick and place settings!
With the ABP we propose a physical benchmark for robotic picking: overall design, objects, configuration, and guidance on appropriate technologies to solve it. Challenges are an important way to drive progress but they occur only occasionally and the test conditions are difficult to replicate outside the challenge. This benchmark is motivated by experience in the recent Amazon Picking Challenge and contains a commonly-available shelf, 42 objects, a set of stencils and standardized task setups.
A major focus through the design of this benchmark was to maximise reproducibility: a number of carefully chosen scenarios with precise instructions on how to place, orient, and align objects with the help of printable stencils are defined. To make the benchmark as accessible as possible to the research community, a white IKEA shelf is used for all picking tasks. Furthermore, we carefully curated a set of 42 objects to ensure global availability and reduced chance of import restrictions.
Team ACRV's experience at #AmazonPickingChallenge 2016Juxi Leitner
Building on Repeatable Grasping Experiments
Team ACRV: Lessons Learned from the Amazon Picking Challenge 2016
Juxi Leitner, ACRV, Queensland University of Technology (Team ACRV, 2016, 2017)
We describe our entry into the 2016 Amazon Picking Challenge (APC) and the lessons learned from deploying a complex, robotic system outside of the lab. To help future developments decided to create a new physical benchmark challenge for robotic picking to drive scientific progress and make research into (end-to-end) picking comparable. It consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils.
A guest lecture about (deep) reinforcement learning and on-going projects includign at QUT. This is for the Machine Learningcourse (CAB 420) at the Queensland University of Technology (QUT)
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http://www.em-ace.eu/en/upload/public-docs/UPORTO_em-ace%20event_agenda.pdf
http://www.em-a.eu/en/home/rss-feed-detail/em-ace-student-event-university-of-porto-16-december-2013-1395.html
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Transferring Spatial Perception Between Robots Operating In A Shared Workspace
1. Jürgen ’Juxi’ Leitner
S. Harding, M. Frank, A. Förster, J. Schmidhuber
istituto dalle molle di studi sull’intelligenza artificiale
università della svizzera italiana
idsia / usi / supsi
transferring
spatial perception
between robots operating
in a shared workspace
#iros 2012