We motivate and present a definition of an embodied, grounded individual sensorimotor interaction history, which captures the time-extended behavior characteristics of humans and many animals. We present an architecture that connects temporally extended individual experience with capacity for action, whereby a robot can develop over ontogeny through interaction. Central to this is an information theoretic metric space of sensorimotor experience, which is dynamically constructed and reconstructed as the robot acts. We present results of robotic experiments that establish the predictive efficacy of the space and we show the robot developing the capacity to play the simple interaction game “peekaboo.” A quantitative investigation of the appropriate horizon length of experience for the game reveals the relationship between the length of experience and the cycle time of interaction, and suggests the importance of multiple, and possibly self-adaptive, horizon lengths.
Arai, S., Sycara, K., & Payne, T.R. (2000). Experience-based reinforcement learning to acquire effective behavior in a multi-agent domain. In Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, Melbourne, Australia, (pp. 125—135). Lecture Notes in Artificial Intelligence Vol. 1886. Berlin: Springer.
2.
Baillie, J.-C. (2005). URBI: Towards a universal robotic low-level programming language. In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada. Piscataway, NJ: IEEE. See http://www.urbiforge.com
3.
Barto, A.G., & Şimşek, Ö. (2005). Intrinsic motivation for reinforcement learning systems. In Proceedings of the 13th Yale Workshop on Adaptive and Learning Systems, New Haven, CT, (pp. 113—118).
4.
Blank, D., Kumar, D., Meeden, L., & Marshall, J. (2005). Bringing up robot: Fundamental mechanisms for creating a self-motivated, self-organizing architecture. Cybernetics andSystems, 36, 125—150.
5.
Bonarini, A., Lazaric, A., Restelli, M., & Vitali, P. (2006). Self-development framework for reinforcement learning agents. In Proceedings of the 5th International Conference on Development and Learning (ICDL 2006), Bloomington, IN. Piscataway, NJ: IEEE.
6.
Braitenberg, V. (1984). Vehicles: experiments in synthetic psychology . Cambridge, MA: MIT Press.
7.
Brooks, A.G., Gray, J., Hoffman, G., Lockerd, A., Lee, H., & Breazeal, C. (2004). Robot's play: interactive games with sociable machines. ACM Computers in Entertainment, 2(3), 10.
8.
Bruner, J.S., & Sherwood, V. (1975). Peekaboo and the learning of rule structures. In J. S. Bruner, A. Jolly, & K. Sylva (Eds.), Play: Its role in development and evolution, (pp. 277—285). New York: Penguin.
9.
Clancey, W.J. (1991). Book review: `The Invention of Memory: A new view of the brain' by Israel Rosenfield. Artificial Intelligence , 50, 241—284.
10.
Clancey, W.J. (1997). Situated cognition: On human knowledge and computer representations. Learning in doing: Cognitive and computational perspectives . Cambridge: Cambridge University Press.
11.
Crutchfield, J.P. (1990). Information and its metric. In L. Lam & H. C. Morris (Eds.), Nonlinear structures in physical systems: Pattern formation, chaos and waves, (pp. 119— 130). New York: Springer.
12.
Dautenhahn, K. (1996). Embodied cognition in animals and artifacts. In Proceedings of AAAI FS Embodied Cognition and Action, (pp. 27—32). Technical report FS-96-02. Menlo Park, CA: AAAI Press .
13.
Dautenhahn, K., Bond, A.H., Cañamero, L., & Edmonds, B. (2002). (Eds.). Socially intelligent agents: Creating relationships with computers and robots, Vol. 3 of Multi-agent systems, artificial societies, and simulated organizations. Berlin: Springer.
14.
Dautenhahn, K., & Christaller, T. (1996). Remembering, rehearsal and empathy — towards a social and embodied cognitive psychology for artifacts. In S. Ò Nualláin, P. McKevitt, & E. Mac Aogáin (Eds.), Two sciences of the mind: Readings in cognitive science and consciousness, (pp. 257— 282). Philadelphia, PA: John Benjamins North America.
15.
Glenberg, A.M. (1997). What is memory for?Behavioral and Brain Sciences, 20, 1—55.
16.
Gurney, K. (1997). An introduction to neural networks. Boca Raton, FL: CRC Press.
17.
Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communications. Science, 304, 78—80.
18.
Kaplan, F., & Hafner, V.V. (2005). Mapping the space of skills: An approach for comparing embodied sensorimotor organizations. In Proceedings of the 4th IEEE International Conference on Development and Learning (ICDL 05), Osaka, Japan, (pp. 129—134). Piscataway, NJ: IEEE.
19.
Kaplan, F., & Oudeyer, P.-Y. (2007). The progress-drive hypothesis: An interpretation of early imitation. In K. Dautenhahn & C. Nehaniv (Eds.), Models and mechanisms of imitation and social learning: Behavioural, social and communication dimensions. Cambridge: Cambridge University Press (pp. 361—377).
20.
Kelso, S. (1995). Dynamic patterns, the self-organization of brain and behavior. Cambridge, MA: MIT Press.
21.
Kozima, H., Nakagawa, C., & Yano, H. (2005). Using robots for the study of human social development . In Proceedings of the AAAI Spring Symposium on Developmental Robotics, Palo Alto, CA, (pp. 111—114). Menlo Park, CA: AAAI Press.
22.
Lungarella, M., Metta, G., Pfeifer, R., & Sandini, G. (2003). Developmental robotics: A survey. ConnectionScience, 15, 151—190.
23.
Lungarella, M., & Sporns, O. (2005). Information self-structuring: Key principles for learning and development. In Proceedings of the 4th International Conference on Development and Learning, Osaka, Japan, (pp. 25—30). Piscataway, NJ: IEEE.
24.
Matarić, M.J. (1992). Integration of representation into goal-driven behaviour-based robots. IEEE Transactions on Robotics and Automation, 8, 304—312.
25.
Meltzoff, A., & Moore, M. (1997). Explaining facial imitation: A theoretical model . Early Development and Parenting, 6, 179—192.
26.
Michaud, F., & Matarić, M.J. (1998). Learning from history for behavior-based mobile robots in non-stationary conditions. MachineLearning, 31 (1—3), 141—167.
27.
Mirza, N.A., Nehaniv, C.L., Dautenhahn, K., & te Boekhorst, R. (2005a). Using temporal information distance to locate sensorimotor experience in a metric space. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC2005), Edinburgh, Scotland, UK (Vol. 1, pp. 150— 157). Piscataway, NJ: IEEE.
28.
Mirza, N.A., Nehaniv, C.L., Dautenhahn, K., & te Boekhorst, R. (2005b). Using sensory-motor phase-plots to characterize robot—environment interactions. In Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2005), Espoo, Finland, (pp. 581—586). Piscataway, NJ: IEEE.
29.
Montague, D.P.F., & Walker-Andrews, A.S. (2001). Peekaboo: A new look at infant's perception of emotion expression. Developmental Psychology, 37, 826—838.
30.
Nehaniv, C.L. (1999). Narrative for artifacts: Transcending context and self. In P. Sengers & M. Mateas (Eds.), Narrative intelligence: Papers from the 1999 AAAI Fall Symposium, North Falmouth, MA, (pp. 101—104). Menlo Park, CA: AAAI Press.
31.
Nehaniv, C.L. (2005). Sensorimotor experience and its metrics. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, Scotland, UK, (Vol. 1, pp. 142—149). Piscataway, NJ: IEEE.
32.
Nehaniv, C.L., Polani, D., Dautenhahn, K., te Boekhorst, R., & Cañamero, L. (2002). Meaningful information, sensor evolution, and the temporal horizon of embodied organisms. In Artificial life VIII, (pp. 345—349). Cambridge, MA: MIT Press.
33.
Nuxoll, A., & Laird, J.E. (2004). A cognitive model of episodic memory integrated with a general cognitive architecture. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the 6th International Conference on Cognitive Modeling, Pittsburgh, PA, (pp. 220—225). Mahwah, NJ: Lawrence Erlbaum Associates .
34.
Olsson, L., Nehaniv, C.L., & Polani, D. (2004). Sensory channel grouping and structure from uninterpreted sensor data. In 2004 NASA/DoD Conference on Evolvable Hardware , Seattle, WA, (pp. 153—160). Los Alamitos, CA: IEEE Computer Society Press.
35.
Olsson, L., Nehaniv, C.L., & Polani, D. (2005). Discovering motion flow by temporal-informational correlations in sensors. In L. Berthouze, H. Kozima, C. G. Prince, G. Sandini, G. Stojanov, G. Metta, & C. Balkenius (Eds.), Proceedings of the 5th International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (pp. 117—120). Lund University Cognitive Studies, 123. Lund, Sweden: LUCS.
36.
Olsson, L., Nehaniv, C.L., & Polani, D. (2006a). From unknown sensors and actuators to actions grounded in sensorimotor perceptions. ConnectionScience, 18, 121—144.
37.
Olsson, L., Nehaniv, C.L., & Polani, D. (2006b). Measuring informational distances between sensors and sensor integration. In L. M. Rocha, L. S. Yaeger, M. A. Bedau , D. Floreano, R. L. Goldstone, & A. Vespignani (Eds.), Artificial life X. Cambridge, MA: MIT Press.
38.
OpenCV. ( 2000). Open computer vision library (gpl licence). See http://sourceforge.net/projects/opencvlibrary/
39.
Oudeyer, P.-Y., Kaplan, F., Hafner, V.V., & Whyte, A. (2005). The playground experiment: Task-independent development of a curious robot. In D. Bank & L. Meeden (Eds.), Proceedings of the AAAI Spring Symposium on Developmental Robotics, Palo Alto, CA, (pp. 42—47). Menlo Park, CA: AAAI Press.
40.
Pea, R.D. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education and human activity. Journal of the LearningSciences, 13, 423—451.
41.
Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press.
42.
Pierce, D., & Kuipers, B. (1997). Map learning with uninterpreted sensors and effectors . Artificial Intelligence, 92, 169—229.
43.
Quick, T., Dautenhahn, K., Nehaniv, C.L., & Roberts, G. (1999). The essence of embodiment: A framework for understanding and exploiting structural coupling between system and environment. In Proceedings of the 3rd International Conference on Computing Anticipatory Systems (CASYS'99)Liège, Belgium. AIP Conference Proceedings.
44.
Rochat, P., Querido, J.G., and Striano, T. (1999). Emerging sensitivity to the timing and structure of protoconversation in early infancy. Developmental Psychology , 35, 950—957.
45.
Rolls, E.T. (1999). The brain and emotion. Oxford : Oxford University Press.
46.
Rosenfield, I. (1988). The invention of memory: A new view of the brain . New York: Basic Books.
Scheult, M., & Logan, B.2001. Affective vs deliberative agent control. In Proceedings of AISB 2001, University of York , UK, (pp. 1—10). York, UK: Society for the Study of Artificial Intelligence and Simulation of Behaviour .
49.
Shannon, C.E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27, 379—423 and 623—656.
50.
Sutton, R.S., & Barto, A.G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
51.
Tarapore, G., Lungarella, M., & Gómez, G. (2004). Fingerprinting agent—environment interaction via information theory. In F. Groen, N. Amato, A. Bonarini, E. Yoshida, & B. Kröse (Eds.), Proceedings of the 8th International Conference on Intelligent Autonomous Systems, Amsterdam , the Netherlands, (pp. 512—520). IOS Press.
52.
te Boekhorst, I.J.A., Lungarella, M., & Pfeifer, R. (2003). Dimensionality reduction through sensori-motor coordination. In O. Kaynak, E. Alpaydin, E. Öja, & L. Xu (Eds.), Proceedings of the Joint International Conference on Artificial Neural Networks and Neural Information Processing, Istanbul, Turkey, (pp. 496—503). Lecture Notes in Computer Science, Vol. 2114. Berlin: Springer.
53.
Thelen, E., & Smith, L.B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.
54.
Tulving, E. (1983). Elements of episodic memory. Oxford: Clarendon Press.
55.
van Duijn, M., Keijzer, F., & Franken, D. (2006). Principles of minimal cognition: Casting cognition as sensorimotor coordination. Adaptive Behavior, 14, 157—170.
56.
Veatch, T.C. (1998). A theory of humour. International Journal of Humour Research, 11, 161—175.
57.
von Hofsten, C. (1993). Prospective control: A basic aspect of action development. Human Development, 36, 253—270.
58.
Vygotsky, L.S. (1978). Mind and society: The development of higher mental processes. Cambridge, MA: Harvard University Press.
