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Making robots learn to perceive and act with understanding
At IIS we enable autonomous robots to perceive and act flexibly and robustly in unstructured environments, leveraging machine learning methods to build perceptual, motor and reasoning skills.
We seek to answer the question: How can we enable robots to acquire the knowledge and understanding they require to interact sensibly with unstructured environments?
Our research addresses complete perception-action loops, from computer vision to grasping and manipulation, using reactive algorithms and/or cognitive models. Much of our work uses machine learning to enable robots to synthesize and improve complex and robust sensorimotor behavior with experience. Related areas of interest include human-robot interaction, image and video analysis, and visual neuroscience.
2022-06-10 | Simon Haller-Seeber, Patrick Hofmann, and Thomas Gatterer teach a tutorial Was sind Roboter, was macht eine KI? Entwickle deine eigene KI und programmiere unsere Minibots at Campustag BG/BRG Sillgasse, Universität Innsbruck. |
2022-06-02 | Erwan Renaudo contributes a talk Deep Learning for Fast Segmentation of E-waste Devices’
Inner Parts in a Recycling Scenario at ICPRAI 2022, Paris/online. [Abstract]Recycling obsolete electronic devices (E-waste) is a dangerous task for human workers. Automated E-waste recycling is an area of great interest but challenging for current robotic applications. We focus on the problem of segmenting inner parts of E-waste devices into manipulable elements. First, we extend a dataset of hard-drive disk (HDD) components with labelled occluded and non-occluded points of view of the parts, in order to increase the diversity and the quality of the learning data with different angles. We then perform an extensive evaluation with three different state-of-the-art models, namely CenterMask, BlendMask and SOLOv2 (including variants) and two types of metrics: the average precision as well as the frame rate. Our results show that instance segmentation using state-of-the-art deep learning methods can precisely detect complex shapes along with their boundaries, as well as being suited for fast tracking of parts in a robotic recycling system. (Rojas et al. 2022) |
2022-05-20 | Justus Piater appears in the media: ORF
Tirol Podcast (in German). [Abstract]Künstliche Intelligenz (KI) ist bereits heute ein täglicher Begleiter, doch vom universellen Haushaltsroboter sind wir noch weit entfernt. Automatisierte Systeme werden zwar Jobs übernehmen, Maschinen werden aber noch lange nicht die Weltherrschaft an sich reißen, falls überhaupt. Zur Langen Nacht der Forschung spricht Martin Kubin im #derWoche „Maschinenzukunft“ mit Prof. Justus Piater vom Institut für Informatik über Chancen und Gefahren von KI. Er weiß auch, warum KI etwa die Justiz unterstützen kann, aber ein KI-Richter niemals objektiv urteilen könnte. |
2022-05-17 | Erwan Renaudo gives an invited talk A Brief tour of autonomous robots' "Cognition" at Creating Agency and Cognition in Automated Systems: What can we learn from the Octopus? International Hybrid Workshop, Innsbruck. [Abstract]In the design of "Cognitive Robots" software architectures, researchers had to face many organizational problems due to the particular nature of robots. Not only should they manage to achieve complex tasks with long-terms goals, but they must also be reactive to their surroundings to preserve their environment. We will go over several layered control paradigms developed over the history of robotics and see how these can relate to the knowledge about the Octopus' brain organization, and discuss how the Octopus itself can inspire cognitive robotic research. |
2022-05-11 | Justus Piater gives an invited talk Robots That Learn Like Humans? at Pint of
Science, Innsbruck. [Abstract]Wouldn't it be great to have a helper robot that we can task with our annoying chores? How would we teach the robot to perform these tasks? Can we build such robots by equipping them with artificial intelligence? We will discuss how human and machine learning differ and how this impacts what skills, including for communication, our helper robot will be able to learn. |
2022-04-28 | Simon Haller-Seeber contributes a talk STAIR Learning Lab at Robotics in Education 2022, online. |
2022-02-19 | Justus Piater gives an invited talk Learning as a Creative Process in Humans and
Machines at Workshop
on “Agency, Life, and Creativity”, online. [Abstract]Humans constantly engage in creative activity such as finding explanations, improving technology, or solving novel problems in everyday life. Even imitating other humans, e.g. while learning a new skill, is creative in that the imitator has to infer the conceptual structure underlying the observed manifestation and to regenerate the latter from the former. In contrast, current AI-enabled agents are unable to generate and draw upon such conceptual structures. Their learning is mostly driven by statistics, and any ability to produce novelty is largely limited to trial and error. I will try to characterize this fundamental difference between learning in human and artificial agents, and will discuss possible technological approaches that might help close this gap. |
2021-11-24 | Justus Piater is a panelist at a public discussion on Privacy in the Digital Age, online. (Organized by ELSA Innsbruck) |
2021-10-22 | Justus Piater gives an invited talk Robotik und KI in der Medizin – nur Bedarfserweckung? at Medical Update Hall 2021 – Fit für das neue Jahrzehnt, UMIT, Hall in Tirol. |
2021-08-26 | Justus Piater contributes a talk Digital Science at the University of Innsbruck at Aurora Research Conference Digital Society & Global Citizenship, Vrije Universiteit Amsterdam. [Abstract]Growing data processing capacities and progress in analytical methods and artificial intelligence are motivating entire branches of science to raise new questions and to develop new approaches. This digital transformation requires progressive, interdisciplinary synergies between the computational sciences and all other scientific disciplines. The Digital Science Center (DiSC) at the University of Innsbruck meets this challenge by integrating and promoting digitalisation of scientific research. In this talk I will present some examples of interdisciplinary research by DiSC staff researchers who are experts in specific aspects of digital science. |
University of Innsbruck
Department of Computer Science
Technikerstr. 21a
6020 Innsbruck
Austria
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