research:projects
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research:projects [2024/01/25 13:41] Alejandro Agostini |
research:projects [2024/02/19 12:24] (current) Antonio Rodriguez-Sanchez [Completed Projects (Selection)] |
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| ===== Current Projects ===== | ===== Current Projects ===== | ||
| - | [[https://doi.org/10.55776/P36965|{{:research:doi.svg?13|}}]] **PURSUIT** - Purposeful Signal-symbol Relations for Manipulation Planning (Austrian Science Fund (FWF), 2023-2026): Artificial intelligence (AI) task planning approaches permits projecting robotic applications outside industrial settings by automatically generating the required instructions for task execution. However, the abstract representation used by AI planning methods makes it complicated to encode physical constraints that are critical to successfully execute a task: What specific movements are necessary to remove a cup from a shelf without collisions? At which precise point should a bottle be grasped for a stable pouring afterwards? These physical constraints are normally evaluated outside AI planning using computationally expensive trial-and-error strategies. PURSUIT focuses on a new task and motion planning (TAMP) approach where the evaluation of physical constraints for task execution starts at perception stage and propagates through planning and execution using a single heuristic search. The approach is based on a common signal-symbol representation that encodes physical constraints in terms of the “purpose” of object relations in the context of a task: Is the hand-bottle relation adequate for picking up the bottle for a stable pouring? Our TAMP approach aims to quickly render task plans that are physically feasible, avoiding the intensive computations of trial-and-error approaches. | + | [[https://doi.org/10.55776/P36965|{{:research:doi.svg?13|}}]] **PURSUIT** - Purposeful Signal-symbol Relations for Manipulation Planning (Austrian Science Fund (FWF), Principal Investigator Project, 2023-2026): Artificial intelligence (AI) task planning approaches permits projecting robotic applications outside industrial settings by automatically generating the required instructions for task execution. However, the abstract representation used by AI planning methods makes it complicated to encode physical constraints that are critical to successfully execute a task: What specific movements are necessary to remove a cup from a shelf without collisions? At which precise point should a bottle be grasped for a stable pouring afterwards? These physical constraints are normally evaluated outside AI planning using computationally expensive trial-and-error strategies. PURSUIT focuses on a new task and motion planning (TAMP) approach where the evaluation of physical constraints for task execution starts at perception stage and propagates through planning and execution using a single heuristic search. The approach is based on a common signal-symbol representation that encodes physical constraints in terms of the “purpose” of object relations in the context of a task: Is the hand-bottle relation adequate for picking up the bottle for a stable pouring? Our TAMP approach aims to quickly render task plans that are physically feasible, avoiding the intensive computations of trial-and-error approaches. |
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| + | **[[https://innalp.at|INNALP Education Hub]]** ([[https://projekte.ffg.at/projekt/4119035|FFG 4119035, 2021-2024]]) - creates innovative, inclusive, and sustainable teaching and learning projects in the heart of the Alps, systematically testing and scientifically tailoring educational innovations for lasting integration into the education system. The INNALP Education Hub includes (so far) 18 innovation projects, assigned to the three underlying innovation fields, "DigiTech Space," "Media, Inclusion & AI Space," and "Green Space." The researched areas of the project range from digitization and robotics to inclusive artificial intelligence and environmental education. | ||
| + | One of the innovation projects is the Software Testing AI Robotic (STAIR) Lab. The [[https://stair-lab.uibk.ac.at|STAIR Lab]] provides learning materials, workshops, and a simulation environment for minibots. The efforts of the STAIR Learning Lab are dedicated to the goal of establishing robotics, artificial intelligence (AI), and software testing in schools. | ||
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| + | ** DESDET's ** (Desinfection Detective, [[https://www.standort-tirol.at/unternehmen/foerderungen/gefoerderte-k-regio-projekte|K-Regio]]) aim is to develop a procedure to prove the disinfection effect and consequently the correct application (e.g.: compliance with the exposure time specified in the EN test) of chemical and/or physical and/or physical disinfection methods in real time and without increased technical effort. Based on such a new procedure, it should be possible, for example, for quality assurance employees or users of the be able to check the effect of the disinfection steps on site in just a few minutes. Furthermore, the aim is to replace the current gold standard for quality control of disinfection processes (phase 2 stage 2 tests, EN 16615, EN 16616) by the optical and AI methods to be developed. | ||
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| + | **CADS ** (FFG, [[https://www.lo-la.info/cads-update/|Camera Avalanche Detection System]]) proposes a novel approach to automating avalanche detection via analysis of webcam streams with deep learning models. To assess the viability of this approach, we trained convolutional neural networks on a publicly-released dataset of 4090 mountain photographs and achieved avalanche detection F1 scores of 92.9% per image and 64.0% per avalanche. Notably, our models do not require a digital elevation model, enabling straightforward integration with existing webcams in new geographic regions. The paper concludes with findings from an initial case study conducted in the Austrian Alps and our vision for operational applications of trained models. | ||
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| {{:research:imagine-transparent.png?nolink&200 ||}}[[https://www.imagine-h2020.eu|IMAGINE - Robots Understanding Their Actions by Imagining Their Effects ]] (EU H2020, 2017-2021): seeks to enable robots to understand the structure of their environment and how it is affected by its actions. “Understanding” here means the ability of the robot (a) to determine the applicability of an action along with parameters to achieve the desired effect, and (b) to discern to what extent an action succeeded, and to infer possible causes of failure and generate recovery actions. | {{:research:imagine-transparent.png?nolink&200 ||}}[[https://www.imagine-h2020.eu|IMAGINE - Robots Understanding Their Actions by Imagining Their Effects ]] (EU H2020, 2017-2021): seeks to enable robots to understand the structure of their environment and how it is affected by its actions. “Understanding” here means the ability of the robot (a) to determine the applicability of an action along with parameters to achieve the desired effect, and (b) to discern to what extent an action succeeded, and to infer possible causes of failure and generate recovery actions. | ||
research/projects.1706186482.txt.gz · Last modified: 2024/01/25 13:41 by Alejandro Agostini