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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.
2022-02-19 | 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. |
2021-08-24 | Justus Piater contributes a lecture Picking and Placing at 2021 GMAR Summer School, Villa Blanka, Innsbruck. |
2021-07-01 | Justus Piater and Matteo Saveriano give an invited talk Making Robots Learn to Perceive and Act with Understanding at Workshop and Open Lab on Field Robotics - Interdisciplinary aspects of robotics and its applications in outdoor scenarios, NOI Techpark, Bolzano, Italy. |
2021-05-26 | Justus Piater contributes a talk Intelligente und interaktive Systeme at GMAR Robotics Science Talks, online. |
2021-03-24 | Justus Piater gives an invited lecture Machine Learning, Perception, And Abstract Concepts at Invited lecture at Ontario Tech U, Canada, online. [Abstract]With every spectacular achievement of a machine learning system, the long-elusive AI breakthrough is popularly proclaimed to be just around the corner. Most recent successes have been due in large part to massive data and computation, in particular using deep artificial neural networks. But can artificial cognition really be achieved just by further scaling up existing machine-learning techniques? I discuss examples of simple, perceptual problems that are easily solved by humans but very difficult for today's machine learning methods. These problems reflect how humans conceptualize their world. Their mastery is thus likely to be an essential prerequisite for autonomous robots to attain higher levels of cognitive abilities. To get there, a few core issues can be identified that should drive research in cognitive robotics. |
2021-01-28 | Matteo Saveriano gives an invited talk Making robots to learn from human observation? at Showcasing Young Austrian Scholars and Scientists, Austrian cultural forum, Ottawa, online. |
2021-01-14 | Matteo Saveriano gives an invited talk Hierarchical action decomposition and motion learning for the execution of manipulation tasks at Hello Tyrol calling! Robotics Talk online, GMAR, Innsbruck, online. |
University of Innsbruck
Department of Computer Science
Technikerstr. 21a
6020 Innsbruck
Austria
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