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Project | NIFTi

Natural human-robot cooperation in dynamic environments

Natural human-robot cooperation in dynamic environments

NIFTi puts the human factor into cognitive architectures. NIFTi investigates how natural behavior in human-robot cooperation can arise. It operationalizes natural cooperation as balancing operational and cooperation demands in a cognitive architecture (CA), to minimize human cognitive task load and optimize joint work flow.

NIFTi designs CAs by closely coupling cognitive user models to how the architecture understands the environment, how it performs actions, how it communicates. The architecture acquires maps which combine perceptual- and conceptual information. These maps capture where what is in the environment, and project this to how acting is affected. The CA combines projections with cognitive user models and plans to predict why changes in human behavior (due to attention, task load) may occur. The CA uses these predictions to anticipate how it should adapt acting and communication to align with the human. The CA bases planning and execution in a cognitive control model. Control uses attention, characteristics of agent morphology, and skill acquisition, to guide autonomous action execution in a cooperative context. The CA achieves balance by actively interconnecting content across modules. Content in modules is not isolated. In the NIFTi CA design, controllers interconnect content across modules, percolating content changes throughout the CA. Changes guide processing in a module; interconnectivity ensures behavior changes coherently system-wide. Controllers are learnt off- and online, using reinforcement learning and statistical (relational) learning.

Adapting to a human thus permeates the entire architecture. NIFTi focuses on cooperation in the USAR domain, to restrict what actions, forms of communication, and user aspects need to be taken into account. USAR end users join NIFTi to yearly evaluate its approach in real settings, using an integrated CA on a new robot with adaptive active/passive locomotion to jointly explore a disaster area.


  1. DFKI, Saarbrucken, Germany: Language Technology Lab
  2. TNO, Soesterberg, The Netherlands: Human Factors
  3. Fraunhofer, St. Augustin, Germany: Intelligent Analysis- and Information Systems
  4. Bluebotics, Lausanne, Switzerland
  5. ETH Zurich, Zurich, Switzerland: Autonomous Systems Lab
  6. Czech Technical University, Prague, Czech Republic: Center for Machine Perception
  7. La Sapienza University of Rome, Rome, Italy: ALCOR Lab
  8. Fire Department of Dortmund, Dortmund, Germany: Institute for Fire Service and Rescue Technology
  9. Ministry of the Interior, Rome, Italy: Corpo Nazionale Vigli del Fuoco

Publications about the project

  1. Designing, developing, and deploying systems to support human-robot teams in disaster response

    Geert-Jan Kruijff; Ivana Kruijff-Korbayová; Shanker Keshavdas; Benoit Larochelle; Miroslav Janicek; Francis Colas; Ming Liu; François Pomerleau; Roland Siegwart; Mark A. Neerincx; Rosemarijn Looije; Nanja J. J. M. Smets; Tina Mioch; Jurriaan van Diggelen; Fiora Pirri; Mario Gianni; Federico Ferri; Matteo Menna; Rainer Worst; Thorsten Linder; Viatcheslav Tretyakov; Hartmut Surmann; TomᨠSvoboda; Michal Rein¨tein; Karel Zimmermann; TomᨠPetříček; Václav Hlaváč

    In: Advanced Robotics, Vol. 28, No. 23, Pages 1547-1570, Taylor & Francis, London, 12/2014.


EU - European Union

Cognitive Systems, Interaction, Robotics

EU - European Union