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Engineering Predictability with Embodied Cognition (EPEC)

How can multimodal systems sense, learn, and predict future events?

EPEC: engineering predictability with embodied cognition

About

Humans are superior to computers and robots when it comes to perceiving with eyes, ears and other senses as well as combining perception with learned knowledge to choose the best actions. This project aims to develop human-inspired models of behaviour and perception and to show that these models can predict future actions accurately.

Our inspiration comes from embodied cognition, a concept from psychology proposing that our bodies, perceptions, abilities, and form, influences how we think. Our goal is to exploit the form of various systems to develop predictive reasoning models as alternatives to traditional reactive systems. These models will be applied in interdisciplinary fields of music technology and robotics. In music, we aim to provide everyday people new ways to move within musical spaces. Our models learn about their interactions with smartphones to proactively assist with their future actions. In robotics, we are developing robots with dynamic forms that can change their thinking in response to new body shapes.

EPEC explores applications in musical interaction on smartphones and robotics
Musical interaction on smartphones, and robotic systems, are EPEC's application areas for new predictive models.

EPEC is directed by Professor Jim Tørresen, who also leads the ROBIN research group in the Department of Informatics. The project employs two post doctoral fellows, Kai Olav Ellefsen and Charles Martin, and PhD researcher Tønnes Nygaard. The project also includes Associate Professor Kyrre Glette, PhD researcher Jørgen Nordmoen, and a number of masters students in machine learning, robotics and music technology.

Objectives

Design, implement and evaluate multimodal systems that are able to sense, learn and predict future events.

Sub-projects

Master Projects

Researchers from the EPEC group supervise master projects in robotics, music technology, and machine learning. Come work with us on predictive models, embodied interactive systems and new robotic interactions! More information available here.

Funding

Supported by The Research Council of Norway under FRINATEK grant agreement 240862 from 2015 to 2019. The grant funds 1 PhD and 2 post-doc positions (10% of prop. funded).

Publications

  • Charles Patrick Martin & Jim Tørresen (2017). MicroJam: An App for Sharing Tiny Touch-Screen Performances, In Cumhur Erkut (ed.),  Proceedings of the International Conference on New Interfaces for Musical Expression.  Aalborg University Copenhagen.  Chapter.  s 495 - 496
  • Charles Patrick Martin & Jim Tørresen (2017). Exploring Social Mobile Music with Tiny Touch-Screen Performances, In Jukka Pätynen; Vesa Välimäki & Tapio Lokki (ed.),  Proceedings of the 14th Sound and Music Computing Conference 2017.  Aalto University.  ISBN 978-952-60-3729-5.  Conference Paper.  s 175 - 180
  • Kai Olav Ellefsen; Herman Lepikson & Jan Albiez (2017). Multiobjective Coverage Path Planning: Enabling Automated Inspection of Complex, Real-World Structures. Applied Soft Computing.  ISSN 1568-4946.  s 264- 282
  • Kristian Nymoen; Arjun Chandra & Jim Tørresen (2016). Self-awareness in Active Music Systems, In Jim Tørresen; Xin Yao; Marco Platzner; Peter R. Lewis & Bernhard Rinner (ed.),  Self-aware Computing Systems.  Springer.  ISBN 978-3-319-39674-3.  Kapittel 14.  s 279 - 296
  • Justinas Miseikis; Kyrre Glette; Ole Jakob Elle & Jim Tørresen (2016). Automatic Calibration of a Robot Manipulator and Multi 3D Camera System, In Yasuhisa Hirata (ed.),  Proc. of 2016 IEEE/SICE International Symposium on System Integration.  IEEE conference proceedings.  ISBN 978-1-5090-3329-4.  Artikkel.  s 735 - 741
  • Tønnes Frostad Nygaard; Jim Tørresen & Kyrre Glette (2016). Multi-objective Evolution of Fast and Stable Gaits on a Physical Quadruped Robotic Platform, In Stefanos Kollias & Yaochu Jin (ed.),  Proc. of 2016 IEEE Symposium Series on Computational Intelligence (SSCI).  IEEE conference proceedings.  ISBN 978-1-5090-4240-1.  Artikkel.
  • Jim Tørresen; Andreas Høyer Iversen & Ralf Greisiger (2016). Data from Past Patients used to Streamline Adjustment of Levels for Cochlear Implant for New Patients, In Stefanos Kollias & Yaochu Jin (ed.),  Proc. of 2016 IEEE Symposium Series on Computational Intelligence (SSCI).  IEEE conference proceedings.  ISBN 978-1-5090-4240-1.  Artikkel.
  • Andreas Færøvig Olsen & Jim Tørresen (2016). Smartphone Accelerometer Data used for Detecting Human Emotions, In Lipo Wang & Xiang Fei (ed.),  Proceedings of 2016 3rd International Conference on Systems and Informatics.  IEEE conference proceedings.  ISBN 978-1-5090-5520-3.  Artikkel.  s 410 - 415
  • Justinas Miseikis; Kyrre Glette; Ole Jakob Elle & Jim Tørresen (2016). Multi 3D Camera Mapping for Predictive and Reflexive Robot Manipulator Trajectory Estimation, In Yaochu Jin & Stefanos Kollias (ed.),  Proc. of 2016 IEEE Symposium Series on Computational Intelligence (SSCI).  IEEE conference proceedings.  ISBN 978-1-5090-4240-1.  1.

View all works in Cristin

  • Kai Olav Ellefsen (2017). Evolutionary Robotics: Automatic design of robot bodies and control.
  • Kai Olav Ellefsen (2017). Automating Robot Design with Evolutionary Algorithms.
  • Charles Patrick Martin (2017). MicroJam: A Social App for Making Music.
  • Charles Patrick Martin (2017). Virtuosic Interactions / Performing with a Neural iPad Band.
  • Jim Tørresen (2016). Artificial intelligence in autonomous systems.
  • Jim Tørresen (2016). Teknologi som kan tilpasse seg gjennom læring.
  • Jim Tørresen (2016). Smartphone Analysis of Human Behaviour for Interactive Music Systems.
  • Jim Tørresen (2016). Maskiner som tenker. Psykologisk tidsskrift.  ISSN 1501-7508.  20, s 36- 40
  •  (2015). Alan Turing opp til prøve.

View all works in Cristin

Tags: machine learning, robotics, interactive music
Published May 24, 2016 3:38 PM - Last modified May 30, 2017 11:23 AM