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IKTPLUSS-IKT og digital innovasjon

Predictive and Intuitive Robot Companion (PIRC)

Alternative title: Forutseende og intuitiv robotmedhjelper (PIRC)

Awarded: NOK 16.0 mill.

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. The systems will be aware and alert for how to act best given their knowledge about themselves and perception of their environment. Humans anticipate many 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? In PIRC, we will apply our machine learning and robotics expertise, and collaborate with researchers in cognitive neuropsychology, to apply recent models of human prediction to 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. Studies of human perception and decision making are of special relevance to model behaviour and forecast future events and actions. 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 societal impact: physical rehabilitation and home care robot support for older people. So far in the project, we have communicated the project´s goals through invited talks and tutorials at various international conferences. Research has also been done through a master´s student and collaboration with Eindhoven University of Technology, respectively which has led to two peer-reviewed conference articles. The work has been on different prediction models and user studies with senior participants, respectively, to compare different forms of human-robot interaction. We have also hired a PhD student (started August 2021) and a researcher (starting January 2022). Three master students also work on PIRC - relevant master's thesis projects.

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. 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? In PIRC, we will apply our machine learning and robotics expertise, and collaborate with researchers in cognitive psychology, to apply recent models of human prediction to 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. Studies of human perception and decision making are of special relevance to model behaviour and forecast future events and actions. 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. Psychology and biology have inspired several breakthroughs in artificial intelligence, such as the perceptron which underlies deep neural networks. We aim to develop similarly high-impact psychology-inspired predictive models. The unexplored nature of these models offers high potential gains, but significant risk. This is partly mitigated by the two different applications in PIRC, which help to uncover different benefits in the new methods.

Publications from Cristin

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IKTPLUSS-IKT og digital innovasjon