Future Research Perspectives With Autonomous, Intelligent Humanoid Robots [Presentation at EYE Lab Surfing Workshop]

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Future Research Perspectives With Autonomous, Intelligent Humanoid Robots [Presentation at the EYE Lab Surfing Workshop] Presentation of my research and future research avenues that could be pursued within Horizon 2020 or FET projects. http://fet-eye.eu/milan-may-2014

Science

Juxi Leitner
Towards Autonomous, Intelligent Systems
Dalle Molle Institute for AI (IDSIA)

Work
Juxi
Leitner
PhD Informatics / Intelligent Systems
MSc Space Robotics & Automation
BSc Information & Software Engineering
Intelligent (Space) Robots
European Space Agency (ESA)
Erasmus Intelligent Systems
Work (Humanoid) Robot Vision
Instituto Superior Técnico (IST)
Mobility Intelligent Space Systems Laboratory
About Me
Work Autonomous & Intelligent Humanoids
Dalle Molle Institute for AI (IDSIA)
http://Juxi.net

Research Idea
‘making sense
of what we sense’
i
cog
bio
hpc
mat
int

IDSIA Robotics Lab
Juxi.net
Contact Juxi Leitner
juxi@idsia.ch
twitter.com/Juxi
facebook.com/juxi.leitner
Juxi.net
robotics.idsia.ch

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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, ...

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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

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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.

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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.

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Presentation slides from the talk given at the IEEE Aerospace Conference (@IEEEAeroConf) 2016 in Big Sky, Montana, USA. We describe a hopping science payload solution designed to exploit the Moon’s lower gravity to leap up to 20m above the surface. The entire solar-powered robot is compact enough to fit within a 10cm cube, whilst providing unique observation and mission capabilities by creating imagery during the hop. The LunaRoo concept is a proposed payload to fly onboard a Google Lunar XPrize entry. Its compact form is specifically designed for lunar exploration and science mission within the constraints given by PTScientists. The core features of LunaRoo are its method of locomotion – hopping like a kangaroo - and its imaging system capable of unique over-the- horizon perception. The payload will serve as a proof of concept, highlighting the benefits of alternative mobility solutions, in particular enabling observation and exploration of terrain not traversable by wheeled robots. in addition providing data for beyond line-of-sight planning and communications for surface assets, extending overall mission capabilities.

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My presentation at the ICINCO 2014 (the 11th International Conference on Informatics in Control, Automation and Robotics) Abstract: We propose a system incorporating a tight integration between computer vision and robot control modules on a complex, high-DOF humanoid robot. Its functionality is showcased by having our iCub humanoid robot pick-up objects from a table in front of it. An important feature is that the system can avoid obstacles – other objects detected in the visual stream – while reaching for the intended target object. Our integration also allows for non-static environments, i.e. the reaching is adapted on-the-fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. Furthermore we show that this system can be used both in autonomous and tele-operation scenarios.

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presentation during the WCCI 2014 in Beijing, China We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investi- gates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the ‘right’ action, i.e. the action with the best possible improvement of the detector.

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Last December I had the pleasure to take part in the EM-ACE workshop held at the University of Porto, Portugal. I was invited to talk about my experience studying in the "Joint European Master in Space Science and Technology" (SpaceMaster) infront of about 60 students. 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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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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