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PIRC - Predictive and Intuitive Robot Companion

PIRC targets a psychology-inspired computing breakthrough through research combining insight from cognitive psychology with computational intelligence to build models that forecast future events and respond dynamically.

Robot assistant

1113194150 © Miriam Doerr Martin Frommherz | shutterstock.com

About the project

The systems will be aware and alert for how to best act given their knowledge about themselves and perception of their environment. Humans anticipate future events more effectively than computers. We combine sensing across multiple modalities with learned knowledge to predict outcomes and choose the best actions. Can we transfer these skills to intelligent systems in human-interactive scenarios?

Artificial intelligence meets cognitive neuropsychology

In PIRC, we will apply our machine learning and robotics expertise, and collaborate with researchers in cognitive psychology. The goal is to apply recent models of human prediction and intuitive action on perception-action loops of future intelligent robot companions.

Our work will allow such robots to adapt and act more seamlessly with their environment than the current technology. We will equip the robots with these new skills and in addition, provide them with the knowledge that users they are interacting with, apply the same mechanisms. This will include mechanisms for adaptive response time from quick and intuitive to slower and well-reasoned. The models will be applied in two robotics applications with potential for very wide impact: physical rehabilitation and home care robot support for older people.

See more information and ROBIN student master projects here.

Publications

  • van Otterdijk, Marieke; Neggers, Margot; Tørresen, Jim & Barakova, Emilia Ivanova (2021). Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior, Proceedings of the 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Virtual Conference, July 8-10, 2021. IEEE Robotics and Automation Society. ISSN 978-1-6654-4952-6.
  • Gorton, Patrick & Ellefsen, Kai Olav (2021). Evaluating Predictive Deep Learning Models. In Yildirim Yayilgan, Sule; Sanfilippo, Filippo & Bajwa, Imran Sarwar (Ed.), Proceeding of the 3rd International Conference on Intelligent Technologies and Applications (INTAP). Springer. ISSN 978-3-030-71711-7. p. 139–150. Full text in Research Archive

View all works in Cristin

  • Tørresen, Jim (2021). Tutorial: Ethical Considerations in Robotics and Automation.
  • Tørresen, Jim (2021). Kunstig intelligens – allsidig i metoder og anvendelser.
  • Tørresen, Jim (2021). INTROMAT: INtroducing personalized TReatment Of Mental health problems using Adaptive Technology.
  • Saplacan, Diana; Tørresen, Jim; Mahler, Tobias & Fosch-Villaronga, Eduard (2021). Robots and Society: Ethical, Legal, and Technical Perspectives on Integrating Robots in the Home- and Healthcare Systems and Services (RO-SO).
  • Tørresen, Jim; Saplacan, Diana; Lintvedt, Mona Naomi; Mahler, Tobias & Fosch-Villaronga, Eduard (2021). Tutorial: Ethical and Legal Assessments Related to Robots and Systems .
  • Tørresen, Jim (2021). Tutorial: Explainability, Trust and Ethics for Robots and Autonomous Systems.
  • Tørresen, Jim (2021). Tutorial: Intelligent System Research – AI Ethical Challenges and Opportunities.
  • Tørresen, Jim (2021). Tutorial: Ethical Considerations in User Modeling and Personalization.
  • Tørresen, Jim (2021). How to achieve ethical artificial intelligence (AI) research and development?
  • Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2021). Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning. IEEE Access. ISSN 2169-3536. doi: 10.1109/ACCESS.2021.3117667.
  • Saplacan, Diana (2021). Ethics and Technology.
  • Tørresen, Jim (2020). Tutorial: Ethical Challenges and Opportunities within Computational Intelligence System Development.
  • Tørresen, Jim (2020). Ethical Consideration in Robotics and Intelligent Systems Research.
  • Ellefsen, Kai Olav (2020). Hva kan intelligente maskiner lære av biologisk liv? Biolog. ISSN 0801-0722. p. 16–19.
  • Thoresen, Sindre & Ellefsen, Kai Olav (2021). Solving Long Term Planning Problems with Direct Future Prediction. Universitetet i Oslo.
  • Gorton, Patrick & Ellefsen, Kai Olav (2020). Backpropagating to the Future: Evaluating Predictive Deep Learning Models. Universitetet i Oslo.
  • Sørensen, Scott Andreas Fiskerstrand & Ellefsen, Kai Olav (2020). Comparing Model-Free and Model-Based Reinforcement Learning for Collision Avoidance. Universitetet i Oslo.

View all works in Cristin

Tags: robotics, artificial intelligence, robot assistance, rehabilitation
Published Oct. 11, 2020 7:17 AM - Last modified May 1, 2021 1:00 PM