Cognitive Processing

, 12:367 | Cite as

Research on cognitive robotics at the Institute of Cognitive Sciences and Technologies, National Research Council of Italy

  • Giovanni Pezzulo
  • Gianluca Baldassarre
  • Amedeo Cesta
  • Stefano Nolfi
Laboratory Note

Keywords

Humanoid Robot Robot Interaction Anticipatory Behavior Cognitive Robotic Distribute Constraint Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Baldassarre G et al. (2009) The IM-CLeVeR project: intrinsically motivated cumulative learning versatile robots. In Lola C, Pierre-Yves O, Christian B (eds) In: Proceedings of the ninth international conference on epigenetic robotics (EpiRob2009). Lund University Cognitive Studies, vol 146. Lund University, Lund, pp 189–190Google Scholar
  2. Baldassarre G, Trianni V, Bonani M, Mondada F, Dorigo M, Nolfi S (2007) Self-organised coordinated motion in groups of physically connected robots. IEEE Trans Syst Manand Cybern 37(1):224–239CrossRefGoogle Scholar
  3. Barsalou LW (1999) Perceptual symbol systems. Behav Brain Sci 22:577–600PubMedGoogle Scholar
  4. Barsalou LW (2008) Grounded cognition. Annu Rev Psychol 59:617–645Google Scholar
  5. Bowerman M, Levinson S (2001) Language acquisition and conceptual development. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. Caligiore D, Guglielmelli E, Parisi D, Baldassarre, G (2010a) A reinforcement learning model of reaching integrating kinematic and dynamic control in a simulated arm robot. In Kuipers B, Shultz T, Stoytchev A, Yu C (eds), IEEE international conference on development and learning (ICDL 2010). IEEE, Piscataway, pp 211–218Google Scholar
  7. Caligiore D, Borghi AM, Mirolli M, Parisi D, Baldassarre G (2010b) A bioinspired hierarchical reinforcement learning architecture for modeling learning of multiple skills with continuous state and actions. In Johansson B, Sahin E and Balkenius C (eds), In: Proceedings of the tenth international conference on epigenetic robotics (EpiRob2010). Lund university cognitive studies, vol 149. Lund University, Lund, pp 27–34Google Scholar
  8. Caligiore D, Borghi A, Parisi, D, Baldassarre G (in press) A computational embodied neuroscience model of compatibility effects. Psychol RevGoogle Scholar
  9. Cangelosi A, Parisi D (1998) The emergence of a language in an evolving population of neural networks. Connect Sci 10(2):83–97CrossRefGoogle Scholar
  10. Cangelosi A, Parisi D (eds) (2002) Simulating the evolution of language. Verlag, LondonGoogle Scholar
  11. Cangelosi A, Metta G, Sagerer G, Nolfi S, Nehaniv C, Fischer K, Tani J, Belpaeme T, Sandini G, Fadiga L, Wrede B, Rohlfing K, Tuci E, Dautenhahn K, Saunders J, Zeschel A (2010) Integration of action and language knowledge: a roadmap for developmental robotics. IEEE Trans Auton Ment Dev 2(3):167–195CrossRefGoogle Scholar
  12. Cappa S, Perani D (2003) The neural correlates of noun and verb processing. J Neuroling 16(2–3):183–189CrossRefGoogle Scholar
  13. Cesta A, D’Aloisi D (1999) Mixed-initiative issues in an agent-based meeting scheduler. User Model User Adap Inter 9(1–2):45–78CrossRefGoogle Scholar
  14. Cesta A, Fratini S (2008) The timeline representation framework as a planning and scheduling software development environment. In: PlanSIG-08 proceedings of the 27th workshop of the UK planning and scheduling special interest group, Edinburgh, December 11–12Google Scholar
  15. Cesta A, Cortellessa G, Pecora F, Rasconi R (2007a) Supporting interaction in the robocare intelligent assistive environment. In: Proceedings of AAAI spring symposium on interaction challenges for intelligent assistants, Stanford, CAGoogle Scholar
  16. Cesta A, Cortellessa G, Denis M, Donati A, Fratini S, Oddi A, Policella N, Rabenau E, Schulster J (2007b) MEXAR2: AI solves mission planner problems. IEEE Intell Syst 22(4):12–19CrossRefGoogle Scholar
  17. Cesta A, Cortellessa G, Giuliani MV, Pecora F, Scopelliti M, Tiberio L (2007c) Psychological implications of domestic assistive technology for the elderly. Psychol J 5(3):229–252Google Scholar
  18. Cesta A, Finzi A, Fratini S, Orlandini A, Tronci E (2010a) Validation and verification issues in a timeline-based planning system. Knowl Eng Rev 25(3):299–318CrossRefGoogle Scholar
  19. Cesta A, Coradeschi S, Cortellessa G, Gonzalez J, Tiberio L, Von Rump S (2010b) Enabling social interaction through embodiment in ExCITE. In ForItAAL. Second Italian forum on ambient assisted living, Trento, October 5–7Google Scholar
  20. Cesta A, Cortellessa G, Rasconi R, Pecora F, Scopelliti M, Tiberio L (2011) Monitoring older people with the RoboCare domestic environment: interaction synthesis and user evaluation. Comput Intell 27(1):60–82CrossRefGoogle Scholar
  21. Christiansen MH, Kirby S (2003) Language evolution: the hardest problem in science? In: Christiansen MH, Kirby S (eds) Language evolution: the states of the art. Oxford University Press, OxfordCrossRefGoogle Scholar
  22. Cortellessa G, Cesta A (2006) Evaluating mixed-initiative systems: an experimental approach. In ICAPS-06. In: Proceedings of the 16th international conference on automated planning and schedulingGoogle Scholar
  23. Cortellessa G, Scopelliti M, Tiberio L, Koch Svedberg G, Loutfi, A, Pecora F (2008) A cross-cultural evaluation of domestic assistive robots. In: Proceedings of AAAI fall symposium on AI in eldercare: new solutions to old problems, November 7–9, ArlingtonGoogle Scholar
  24. Cortellessa G, D’Amico R, Pagani M, Tiberio L, De Benedictis R, Bernardi G, Cesta A (2011) Modeling users of crisis training environments by integrating psychological and physiological data. In: Proceedings of IEA/AIE-11, Siracuse, JuneGoogle Scholar
  25. De Greef J, Nolfi S (2010) Evolution of implicit and explicit communication in a group of mobile robots. In: Nolfi S, Mirolli M (eds) Evolution of communication and language in embodied agents. Verlag, BerlinGoogle Scholar
  26. Dominey P (2006) From holophrases to abstract grammatical constructions: insights from simulation studies. In: Clark E, Kelly B (eds) Constructions in acquisition. CSLI Publications, Stanford, pp 137–162Google Scholar
  27. Elman J (2006) Computational approaches to language acquisition. In: Brown K (ed) In encyclopedia of language and linguistics, vol 2, 2nd edn. Elsevier, Oxford, pp 726–732Google Scholar
  28. Fratini S, Pecora F, Cesta A (2008) Unifying planning and scheduling as timelines in a component-based perspective. Arch Control Sci 18(2):231–271Google Scholar
  29. Gallese V (2008) Mirror neurons and the social nature of language: the neural exploitation hypothesis. Soc Neurosci 3:317–333PubMedCrossRefGoogle Scholar
  30. Glenberg A (1997) What memory is for? Behav Brain Sci 20:1–55PubMedGoogle Scholar
  31. Glenberg A, Kaschak M (2002) Grounding language in action. Psychon Bull Rev 9:558–565PubMedCrossRefGoogle Scholar
  32. Goldberg A (2006) Constructions at work: the nature of generalization in language. Oxford University Press, OxfordGoogle Scholar
  33. Goldberg A (2009) Constructions work. Cogn Ling 20(1):201–224CrossRefGoogle Scholar
  34. Grush R (2004) The emulation theory of representation: motor control, imagery, and perception. Behav Brain Sci 27:377–396PubMedGoogle Scholar
  35. Haller S, McRoy A, Kobsa S (1999) Computational models of mixed-initiative interaction. Kluwer Academic Publishers, BerlinGoogle Scholar
  36. Hommel B, Musseler J, Aschersleben G, Prinz W (2001) The theory of event coding: a framework for perception and action planning. Behav Brain Sci 24(5):849–878PubMedCrossRefGoogle Scholar
  37. Hutchins E, Johnson C (2009) Modelling the emergence of language as an embodied collective cognitive activity. Top Cogn Sci 1:523–546CrossRefGoogle Scholar
  38. Jeannerod M (2006) Motor cognition. Oxford University Press, OxfordCrossRefGoogle Scholar
  39. Kaplan F, Oudeyer P, Bergen B (2008) Computational models in the debate over language learn ability. Infant Child Dev 17(1):55–80CrossRefGoogle Scholar
  40. Kirby S (2002) Natural language from artificial life. Artif Life 8(2):185–215PubMedCrossRefGoogle Scholar
  41. Lakoff G (1987) Women, fire, and dangerous things: what categories reveal about the mind. University of Chicago Press, ChicagoGoogle Scholar
  42. Langacker R (1987) Foundations of cognitive grammar. Stanford University Press, StanfordGoogle Scholar
  43. Langacker RW (2000) A dynamic usage-based model. In: Barlow M, Kemmer S (eds) In usage-based models of language. CSLI Publications, Stanford, pp 1–63Google Scholar
  44. Langacker R (2008) Cognitive grammar: a basic introduction. Oxford University Press, OxfordGoogle Scholar
  45. MacWhinney B (2005) The emergence of linguistic form in time. Connect Sci 17(3–4):191–211CrossRefGoogle Scholar
  46. MacWhinney B (2010) Computational models of child language learning: an introduction. J Child Lang 37:477–485PubMedCrossRefGoogle Scholar
  47. Mannella F, Mirolli M, Baldassarre G (2010) The interplay of pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat. In: Tosh C, Ruxton G (eds) Modelling perception with artificial neural networks. Cambridge University press, Cambridge, pp 93–113CrossRefGoogle Scholar
  48. Massera G, Tuci E, Ferrauto T, Nolfi S (in press). The facilitator role of linguistic instructions on developing manipulation skills. IEEE Comp Intel MagGoogle Scholar
  49. McGann C, Py F, Rajan K, Ryan JP, Henthorn R (2008) Adaptive control for autonomous underwater vehicles. In: AAAI-08 proceedings of the 23rd AAAI conference on artificial intelligence, ChicagoGoogle Scholar
  50. Mirolli M, Parisi D (2008) How producer biases can favor the evolution of communication: an analysis of evolutionary dynamics. Adapt Behav 16(1):27–52CrossRefGoogle Scholar
  51. Mirolli M, Ferrauto T, Nolfi S (2010a) Categorisation through evidence accumulation in an active vision system. Connect Sci 22(4):331–354CrossRefGoogle Scholar
  52. Mirolli M, Mannella F, Baldassarre G (2010b) The roles of the amygdala in the affective regulation of body, brain, and behaviour. Connect Sci 22(3):215–245CrossRefGoogle Scholar
  53. Muscettola N, Dorais GA, Fry C, Levinson R, Plaunt C (2002) IDEA: planning at the core of autonomous reactive agents. In: Proceedings of the 3rd international NASA workshop on planning and scheduling for space, OctoberGoogle Scholar
  54. Nolfi S (2005) Emergence of communication in embodied agents: co-adapting communicative and non-communicative behaviours. Connect Sci 17(3–4):231–248CrossRefGoogle Scholar
  55. Nolfi S, Mirolli M (2010) Evolution of communication and language in embodied agents. Verlag, BerlinCrossRefGoogle Scholar
  56. Oddi A, Cesta A (2000) Toward interactive scheduling systems for managing medical resources. Artif Intell Med 20(2):113–138PubMedCrossRefGoogle Scholar
  57. Pecora F, Cesta A (2007) DCOP for smart homes: a case study. Comput Intell 23(4):395–419CrossRefGoogle Scholar
  58. Pezzulo G (2008) Coordinating with the future: the anticipatory nature of representation. Mind Mach 18:179–225CrossRefGoogle Scholar
  59. Pezzulo G (2009) DiPRA: a layered agent architecture which integrates practical reasoning and sensorimotor schemas. Connect Sci 21:297–326CrossRefGoogle Scholar
  60. Pezzulo G (2011) Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind Lang 26:78–114Google Scholar
  61. Pezzulo G, Calvi G (2007) Designing modular architectures in the framework AKIRA. Multiagent Grid Syst 3:65–86Google Scholar
  62. Pezzulo G, Calvi G (in press) Computational explorations of perceptual symbol system theory. New Ideas PsycholGoogle Scholar
  63. Pezzulo G, Castelfranchi C (2007) The Symbol Detachment Problem. Cogn Process 8:115–131Google Scholar
  64. Pezzulo G, Castelfranchi C (2009) Thinking as the control of imagination: a conceptual framework for goal directed systems. Psychol Res 73:559–577PubMedCrossRefGoogle Scholar
  65. Pezzulo G, Barsalou L, Cangelosi A, Fischer M, McRae K, Spivey M (2011) The mechanics of embodiment: a dialogue on embodiment and computational modeling. Front Cogn 2(5):1–21Google Scholar
  66. Pollack ME (2005) Intelligent technology for an aging population: the use of AI to assist elders with cognitive impairment. AI Mag 26(2):9–24Google Scholar
  67. Pulvermuller F (2003) The neuroscience of language. on brain circuits of words and serial order. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  68. Rizzolatti G, Arbib M (1998) Language within our grasp. Trends Neurosci 21:188–194PubMedCrossRefGoogle Scholar
  69. Santucci VG, Baldassarre G, Mirolli M (2010). Biological cumulative learning requires intrinsic motivation: a simulated robotic study on the development of visually-guided reaching. In: Proceedings of the tenth international conference on epigenetic robotics (EpiRob2010). Sweden, November 5–7, 2010Google Scholar
  70. Scopelliti M, Giuliani MV, Fornara F (2005) Robots in a domestic setting: a psychological approach. Univers Access Inf Soc 4(2):146–155Google Scholar
  71. Sperati V, Trianni V, Nolfi S (2010). Evolution of self-organised path formation in a swarm of robots. In Dorigo M, Birattari M, Di Caro GA, Doursat R, Engelbrecht AP, Floreano D, Gambardella LM, Groß R, Şahin E, Stützle Th, Sayama H (eds). In: Proceedings of the 7th international conference on swarm intelligence (ANTS 2010), vol 6234 of lecture notes in computer science. Verlag, Berlin, pp 165–166Google Scholar
  72. Steels L (1997) The synthetic modeling of language origins. Evol Commun J 1(1):1–34CrossRefGoogle Scholar
  73. Steels L (2003) Evolving grounded communication for robots. Trends Cogn Sci 7(7):308–312PubMedCrossRefGoogle Scholar
  74. Tiberio L, Padua L, Pellegrino AR, Aprile I, Cortellessa G, Cesta A (2011) Assessing the tolerance of a telepresence robot in users with mild cognitive impairment. In: Proceedings on HRI 2011 workshop on social robotic telepresence, Lausanne, MarchGoogle Scholar
  75. Tomasello M (2003) Constructing a language: an usage-based theory of language acquisition. Harvard University, CambridgeGoogle Scholar
  76. Trianni T, Nolfi S (2009) Self-organising sync in a robotic swarm: a dynamical system view. IEEE Transact Evol Comput 13(4):722–741CrossRefGoogle Scholar
  77. Trianni V, Nolfi S, Dorigo M (2006) Cooperative hole-avoidance in a swarm-bot. Rob Auton Syst 54(2):97–103CrossRefGoogle Scholar
  78. Tuci E, Ferrauto T, Massera G, Nolfi S (2010a) The evolution of behavioural and linguistic skills to execute and generate two-word instructions in agents controlled by dynamical neural networks. In: Proceedings of the 12th international conference on the synthesis and simulation of living systems (ALife XII), OdenseGoogle Scholar
  79. Tuci E, Ferrauto T, Massera G, Nolfi S (2010b) Co-development of linguistic and behavioural skills: compositional semantics and behaviour generalisation. In: Proceedings of the 11th international conference on simulation of adaptive behavior (SAB2010)Google Scholar
  80. Tuci E, Massera G, Nolfi S (2010c) Active categorical perception of object shapes in a simulated anthropomorphic robotic arm. IEEE Transac Evol Comput J 14(6):885–899CrossRefGoogle Scholar
  81. Tuci E, Ferrauto T, Zeschel A, Massera G, Nolfi S (in press) An experiment on behaviour generalisation and the emergence of linguistic compositionality in evolving robots. IEEE Trans Auton Ment DevGoogle Scholar
  82. Venditti A, Mirolli M, Parisi D, Baldassarre G (2009) In Serra R, Villani M, Poli I (eds) Artificial life and evolutionary computation. In: Proceedings of wivace 2008. A neural-network model of the dynamics of hunger, learning, and action vigor in mice, pp 131–142. World Scientific, CaliforniaGoogle Scholar
  83. Wermter S, Page M, Knowles M, Gallese V, Pulvermller F, Taylor J (2009) Multimodal communication in animals, humans and robots: an introduction to perspectives in brain-inspired informatics. Neural Netw 22(2):111–115PubMedCrossRefGoogle Scholar
  84. Wolpert DM, Gharamani Z, Jordan M (1995) An internal model for sensorimotor integration. Science 269:1179–1182CrossRefGoogle Scholar
  85. Wolpert DM, Doya K, Kawato M (2003) A unifying computational framework for motor control and social interaction. Philos Trans R Soc Lond B Biol Sci 358:593–602PubMedCrossRefGoogle Scholar

Copyright information

© Marta Olivetti Belardinelli and Springer-Verlag 2011

Authors and Affiliations

  • Giovanni Pezzulo
    • 1
    • 2
  • Gianluca Baldassarre
    • 1
  • Amedeo Cesta
    • 1
  • Stefano Nolfi
    • 1
  1. 1.Institute of Cognitive Sciences and Technologies, National Research CouncilRomeItaly
  2. 2.Istituto di Linguistica Computazionale “Antonio Zampolli” ‐ Consiglio Nazionale delle RicerchePisaItaly

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