ELSA - Effective Learning of Social Affordances for human-robot interaction
Affordances are action opportunities directly perceived by an agent to interact with its environment. The concept is gaining interest in robotics, where it offers a rich description of the objects and the environment, focusing on the potential interactions rather than the sole physical properties. In this project, we extend this notion to social affordances. The goal is for robots to autonomously learn not only the physical effects of interactive actions with humans, but also the humansʼ reactions they produce (emotion, speech, movement). For instance, pointing and gazing in the same direction make humans orient towards the pointed direction, while pointing and looking at the finger make humans look at the finger. Besides, scratching the robotʼs chin makes some but not all humans smile. The project will investigate how learning human-general and human-specific social affordances can enrich a robotʼs action repertoire for human-aware task planning and efficient human-robot interaction. It is carried out in cooperation with ISIR, Sorbonne University and LAAS-CNRS, France.
Principal investigators: Justus Piater, Matthias Schurz, Erwan Renaudo
Funding agencies: Austrian Science Fund (FWF) and the French National Research Agency (ANR)
ArtiPro - Artificial intelligence for personalized medicine in depression
The aim of this project is to establish an artificial intelligence platform that captures, integrates, analyzes and harmonizes data from clinical research on biomarker signatures and therapeutic outcome from high-quality multidisciplinary sources with the purpose of identifying robust multimodal biomarkers and outcomes for affective disorders. The platform shall focus on the area of antidepressant treatment in affective disease and the definition of functional imaging biomarkers in combination with molecular data (transcriptomics and genetics).
Principal investigators: Roberto Viviani, Clara Rauchegger
Funding agencies: ERA PerMed (ERA-Net Personalized Medicine), European Commission and FWF
DIGIdat - Digital data analysis of indoor air quality meets ESD
Indoor air quality in schools has become a highly discussed topic in the wake of the Corona pandemic. But what is the best way to ventilate a classroom? To what extent does installing a ventilation system improve indoor air quality, and how do comparatively simple interventions (CO2 traffic light signal, awareness raising, etc.) fare? In the research project DIGIdat, researchers from the University of Innsbruck and the University College of Teacher Education Tyrol are working with students and their teachers to investigate the interplay between indoor air quality, thermal comfort, energy efficiency and awareness raising. Employing a Citizen Science approach, the project intends to actively involve a total of about 750 students at 10 Tyrolean schools in collecting measurement data.
Principal investigator: Gabriel Rojas-Kopeinig
Funding agency: OEAD - Sparkling Science