Research on Cognitive Robotics at the Institute of Cognitive Sciences and Technologies, National Research Council of Italy (ISTC-CNR)

The Institute of Cognitive Sciences and Technologies of the National Research Council (ISTC-CNR) (http://www. istc.cnr.it/) is the most important Italian research institution on cognitive science. It includes more than 60 scientists involved in high interdisciplinary research ranging from cognitive science and robotics to linguistics and primatology. The headquarters of the institute are in Rome, but branches of the institute are also in Padua and Trento. ISTC-CNR is a research hub in cognitive robotics as this is the main research focus of several research labs working within it. The interdisciplinary approach used is one of the key characteristics of cognitive robotics studies at ISTC-CNR. This research involves over 30 people (among researchers, Post-Docs, and PhD students) having different backgrounds (ranging from engineering and computer science to psychology, neuroscience, and philosophy) and pursuing research objectives as diverse as (a) the use of computational and robotic models to investigate psychological and neural phenomena, (b) the realization of novel paradigms for robot learning, control, planning, decisionmaking, team making, and human–robot interaction, and (c) the delivery of novel autonomous robotic technologies that act in real-world scenarios. The interdisciplinary approach used has advantages, in that the broadness of methodologies that we employ, and our extensive networks of worldwide collaborations, allows for significant crossfertilizations and hybridizations. This is making ISTC-CNR an ideal venue to develop novel ideas and paradigms that eschew the traditional disciplinary boundaries and permits our research initiatives to have significant impact in several different fields: scientific, social, technological, and industrial. A second key characteristic of cognitive robotics studies at ISTC-CNR is its broad coverage of many interconnected research lines, all of which are at the interface of robotics and cognitive science, and addresses open problems within them. Hereby, we summarize the main research lines that are currently active at ISTC-CNR.

[1]  Frederic Py,et al.  Adaptive Control for Autonomous Underwater Vehicles , 2008, AAAI.

[2]  Edwin Hutchins,et al.  Modeling the Emergence of Language as an Embodied Collective Cognitive Activity , 2009, Top. Cogn. Sci..

[3]  Ferdinando Fornara,et al.  Robots in a domestic setting: a psychological approach , 2005, Universal Access in the Information Society.

[4]  R. Langacker.  Foundations of cognitive grammar , 1983 .

[5]  Eve V. Clark,et al.  Constructions in acquisition , 2006 .

[6]  Cristiano Castelfranchi,et al.  The symbol detachment problem , 2007, Cognitive Processing.

[7]  D. Parisi,et al.  TRoPICALS: a computational embodied neuroscience model of compatibility effects. , 2010, Psychological review.

[8]  Domenico Parisi,et al.  A NEURAL-NETWORK MODEL OF THE DYNAMICS OF HUNGER, LEARNING, AND ACTION VIGOR IN MICE , 2009 .

[9]  Marco Mirolli,et al.  Biological Cumulative Learning through Intrinsic Motivations: A Simulated Robotic Study on the Development of Visually-Guided Reaching , 2010, EpiRob.

[10]  A. Glenberg,et al.  What memory is for: Creating meaning in the service of action , 1997, Behavioral and Brain Sciences.

[11]  Stefano Nolfi,et al.  Evolution of Implicit and Explicit Communication in Mobile Robots , 2010, Evolution of Communication and Language in Embodied Agents.

[12]  Ronald W. Langacker,et al.  Cognitive Grammar: A Basic Introduction , 2008 .

[13]  Graeme D. Ruxton,et al.  Modelling Perception with Artificial Neural Networks: General themes , 2010 .

[14]  D. Perani,et al.  The neural correlates of noun and verb processing , 2003, Journal of Neurolinguistics.

[15]  M. Arbib,et al.  Language within our grasp , 1998, Trends in Neurosciences.

[16]  Enrico Tronci,et al.  Validation and verification issues in a timeline-based planning system , 2010, The Knowledge Engineering Review.

[17]  Domenico Parisi,et al.  A Bioinspired Hierarchical Reinforcement Learning Architecture for Modeling Learning of Multiple Skills with Continuous States and Actions , 2010, EpiRob.

[18]  Lorenza Tiberio,et al.  Assessing the tolerance of a telepresence robot in users with Mild Cognitive Impairment – A protocol for studying users ’ physiological response – , 2011 .

[19]  Giulio Sandini,et al.  In Press, Ieee Transactions on Autonomous Mental Development , 2010 .

[20]  K. Doya,et al.  A unifying computational framework for motor control and social interaction. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[21]  Stefano Nolfi,et al.  Emergence of communication in embodied agents: co-adapting communicative and non-communicative behaviours , 2005, Connect. Sci..

[22]  Jean-Christophe Verstraete.  Language Acquisition and Conceptual Development (review) , 2005 .

[23]  S. Nolfi,et al.  Self-Organising Sync in a Robotic Swarm , 2007 .

[24]  Peter Ford Dominey.  From Holophrases to Abstract Grammatical Constructions : Insights from Simulation Studies , 2004 .

[25]  F. Pulvermüller.  The Neuroscience of Language , 2003 .

[26]  Amedeo Cesta,et al.  Modeling Users of Crisis Training Environments by Integrating Psychological and Physiological Data , 2011, IEA/AIE.

[27]  Amedeo Cesta,et al.  Mexar2: AI Solves Mission Planner Problems , 2007, IEEE Intelligent Systems.

[28]  G. Aschersleben,et al.  The theory of event coding (TEC): A framework of perception and action , 2001 .

[29]  Jeffrey L. Elman,et al.  Computational Approaches to Language Acquisition , 2006 .

[30]  Brian Macwhinney,et al.  Computational models of child language learning: an introduction. , 2010, Journal of child language.

[31]  Francesco Mannella,et al.  The roles of the amygdala in the affective regulation of body, brain, and behaviour , 2010, Connect. Sci..

[32]  L. Steels.  Evolving grounded communication for robots , 2003, Trends in Cognitive Sciences.

[33]  Rick Grush,et al.  The emulation theory of representation: Motor control, imagery, and perception , 2004, Behavioral and Brain Sciences.

[34]  Stefano Nolfi,et al.  Co-development of Linguistic and Behavioural Skills: Compositional Semantics and Behaviour Generalisation , 2010, SAB.

[35]  V. Gallese.  Mirror neurons and the social nature of language: The neural exploitation hypothesis , 2008, Social neuroscience.

[36]  Roberto Serra,et al.  Artificial Life and Evolutionary Computation: Proceedings of Wivace 2008 , 2010 .

[37]  Luc Steels,et al.  The synthetic modeling of language origins , 1997 .

[38]  Amedeo Cesta,et al.  The Timeline Representation Framework as a Planning and Scheduling Software Development Environment , 2008 .

[39]  Amedeo Cesta,et al.  DCOP FOR SMART HOMES: A CASE STUDY , 2007, Comput. Intell..

[40]  Michael P. Kaschak,et al.  Grounding language in action , 2002, Psychonomic bulletin & review.

[41]  Julian Togelius,et al.  The IM-CLeVeR Project : Intrinsically Motivated Cumulative Learning Versatile Robots , 2009 .

[42]  Marco Dorigo,et al.  Cooperative hole avoidance in a swarm-bot , 2006, Robotics Auton. Syst..

[43]  G. Pezzulo.  Grounding Procedural and Declarative Knowledge in Sensorimotor Anticipation , 2011 .

[44]  Stefano Nolfi,et al.  The Evolution of Behavioural and Linguistic Skills to Execute and Generate Two-word Instructions in Agents Controlled by Dynamical Neural Networks , 2010, ALIFE.

[45]  Alfred Kobsa,et al.  Computational Models of Mixed-Initiative Interaction , 2007, Springer Netherlands.

[46]  Martha E. Pollack,et al.  Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment , 2005, AI Mag..

[47]  M. Tomasello.  Constructing a Language: A Usage-Based Theory of Language Acquisition , 2003 .

[48]  Stefano Nolfi,et al.  Categorisation through evidence accumulation in an active vision system , 2010, Connect. Sci..

[49]  Nicola Muscettola,et al.  IDEA: Planning at the Core of Autonomous Reactive Agents , 2002 .

[50]  Stefan Wermter,et al.  Multimodal communication in animals, humans and robots: An introduction to perspectives in brain-inspired informatics , 2009, Neural Networks.

[51]  Julian M. Pine,et al.  Constructing a Language: A Usage-Based Theory of Language Acquisition. , 2004 .

[52]  G. Pezzulo,et al.  Thinking as the control of imagination: a conceptual framework for goal-directed systems , 2009, Psychological research.

[53]  Giovanni Pezzulo,et al.  Coordinating with the Future: The Anticipatory Nature of Representation , 2008, Minds and Machines.

[54]  A. Goldberg.  Constructions at Work: The Nature of Generalization in Language , 2006 .

[55]  Amedeo Cesta,et al.  Supporting Interaction in the ROBOCARE Intelligent Assistive Environment , 2007, Interaction Challenges for Intelligent Assistants.

[56]  A. Cesta,et al.  Enabling Social Interaction Through Embodiment in Ex CITE , 2010 .

[57]  G. Lakoff,et al.  Women, Fire, and Dangerous Things: What Categories Reveal about the Mind , 1988 .

[58]  Stefano Nolfi,et al.  Evolution of Self-organised Path Formation in a Swarm of Robots , 2010, ANTS Conference.

[59]  Eugenio Guglielmelli,et al.  A reinforcement learning model of reaching integrating kinematic and dynamic control in a simulated arm robot , 2010, 2010 IEEE 9th International Conference on Development and Learning.

[60]  Stefano Nolfi,et al.  An Experiment on Behavior Generalization and the Emergence of Linguistic Compositionality in Evolving Robots , 2011, IEEE Transactions on Autonomous Mental Development.

[61]  Amedeo Cesta,et al.  Evaluating Mixed-Initiative Systems: An Experimental Approach , 2006, ICAPS.

[62]  Ronald W. Langacker,et al.  Chapter 4: A dynamic usage-based model , 1999 .

[63]  Morten H. Christiansen,et al.  Language Evolution: The Hardest Problem in Science? , 2003 .

[64]  Adele E. Goldberg,et al.  Constructions at Work , 2005 .

[65]  Amy Loutfi,et al.  A Cross-Cultural Evaluation of Domestic Assistive Robots , 2008, AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems.

[66]  Amedeo Cesta,et al.  Toward interactive scheduling systems for managing medical resources , 2000, Artif. Intell. Medicine.

[67]  G. Baldassarre,et al.  Modelling Perception with Artificial Neural Networks: The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat , 2010 .

[68]  Giovanni Pezzulo,et al.  DiPRA: a layered agent architecture which integrates practical reasoning and sensorimotor schemas , 2009, Connect. Sci..

[69]  Friedemann Pulvermüller,et al.  The Neuroscience of Language: On Brain Circuits of Words and Serial Order , 2003 .

[70]  Angelo Cangelosi,et al.  The Emergence of a 'Language' in an Evolving Population of Neural Networks , 1998, Connect. Sci..

[71]  G. Pezzulo,et al.  Computational explorations of perceptual symbol systems theory , 2011 .

[72]  Amedeo Cesta,et al.  MONITORING ELDERLY PEOPLE WITH THE ROBOCARE DOMESTIC ENVIRONMENT: INTERACTION SYNTHESIS AND USER EVALUATION , 2011, Comput. Intell..

[73]  Stefano Nolfi,et al.  Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm , 2010, IEEE Transactions on Evolutionary Computation.

[74]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[75]  Giovanni Pezzulo,et al.  Designing modular architectures in the framework AKIRA , 2007, Multiagent Grid Syst..

[76]  Amedeo Cesta,et al.  Psychological Implications of Domestic Assistive Technology for the Elderly , 2007, PsychNology J..

[77]  Brian MacWhinney,et al.  The emergence of linguistic form in time , 2005, Connect. Sci..

[78]  Angelo Cangelosi,et al.  The Mechanics of Embodiment: A Dialog on Embodiment and Computational Modeling , 2011, Front. Psychology.

[79]  Domenico Parisi,et al.  How Producer Biases Can Favor the Evolution of Communication: An Analysis of Evolutionary Dynamics , 2008, Adapt. Behav..

[80]  Daniela D'Aloisi,et al.  Mixed-Initiative Issues in an Agent-Based Meeting Scheduler , 2004, User Modeling and User-Adapted Interaction.

[81]  Marco Dorigo,et al.  Self-Organized Coordinated Motion in Groups of Physically Connected Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[82]  Simon Kirby,et al.  Natural Language From Artificial Life , 2002, Artificial Life.

[83]  Angelo Cangelosi,et al.  Simulating the Evolution of Language , 2002, Springer London.

[84]  Stefano Nolfi,et al.  The Facilitatory Role of Linguistic Instructions on Developing Manipulation Skills , 2010, IEEE Computational Intelligence Magazine.

[85]  L. Barsalou,et al.  Whither structured representation? , 1999, Behavioral and Brain Sciences.

[86]  Amedeo Cesta,et al.  Unifying planning and scheduling as timelines in a component-based perspective , 2008 .

[87]  Pierre-Yves Oudeyer,et al.  Computational Models in the Debate over Language Learnability , 2008 .