The field of developmental robotics forms a bridge between two research communities: those who study learning and development in humans and those who study comparable processes in artificial systems. The Babybot Challenge is designed to help strengthen this bridge. When submitting your 6-pages paper to ICDL-EPIROB 2017 you can specify that you want it to participate in the challenge. Your paper should describe a computational or robotic model that explains one or several studies from the infant development literature. The developmental studies chosen can be conventional laboratory experiments or studies using naturalistic observation methodologies. In any case, they should be well-designed studies. For older studies, it should be clear that their results have held up over time rather than having been refuted or strongly questioned by more recent evidence. Ideally, the model will be developed in close collaboration with a developmental psychologist who is an expert in the corresponding area.
Submissions will be judged by the following five criteria: (1) How well does the model represent the particular features of the experimental research paradigm? (2) How closely does the performance of the model replicate the findings from the chosen study or studies? (3) How parsimonious is the model? (4) What novel insights or explanations for the observed developmental pattern are generated by the model? And (5) does the model make any interesting and testable predictions?
Authors wishing to participate in the challenge submit their contribution as a regular paper to the ICDL-EPIROB conference, indicating that this is a submission for the Babybot challenge during the submission process and providing a short explanation how their submission addresses the five review criteria above. A few selected contributions will be offered an extended length talk slot at the ICDL-EPIROB conference to present their results, and the final winner will get a prize (surprise!).
Possible topics include but are not limited to:
Specific example studies that lend themselves to computational and robotic modeling can be found in the calls to previous Babybot challenges.
Below we list some possible inspirations for human development studies and computational models (both surveys and specific experiments/models).
Asada et al. (2009) Cognitive Developmental Robotics: A Survey. IEEE Transactions on Autonomous Mental Development. (pdf)
Hoffmann et al. (2010) Body Schema in Robotics: A Review. IEEE Transactions on Autonomous Mental Development. (pdf)
Jamone at al. (2016) Affordances in psychology, neuroscience and robotics: a survey. IEEE Transactions on Cognitive and Developmental Systems. (pdf)
Sommerville, J.A., Woodward, A.L., & Needham, A. (2005) Action experience alters 3-month-old infants’ perception of others’ actions. Cognition, 96, B1-B11. (pdf)
Iverson, J. M., & Goldin-Meadow, S. (2005). Gesture paves the way for language development. Psychological Science, 16, 367-371. (pdf)
von Hofsten, C. (1984). Developmental changes in the organization of prereaching movements. Developmental Psychology, 20, 378-388. (pdf)
Corbetta, D. Thurman, S.L. Wiener, R.F. Guan, Y. and Williams, J.L. (2014) Mapping the feel of the arm with the sight of the object: on the embodied origins of infant reaching Frontiers in Psychology, 5, 00576. pdf
Williams Joshua L, Corbetta Daniela (2016) Assessing the impact of movement consequences on the development of early reaching in infancy. Frontiers in Psychology, 7, 00587. pdf
Moulin-Frier, C., Nguyen, S. M., & Oudeyer, P. Y. (2014). Self-organization of early vocal development in infants and machines: the role of intrinsic motivation.
Schöner, G., & Thelen, E. (2006). Using dynamic field theory to rethink infant habituation. Psychological review, 113(2), 273.
Thelen, E., Schöner, G., Scheier, C., & Smith, L. B. (2001). The dynamics of embodiment: A field theory of infant perseverative reaching. Behavioral and brain sciences, 24(01), 1-34.
Triesch, J., Teuscher, C., Deák, G. O., & Carlson, E. (2006). Gaze following: why (not) learn it?. Developmental science, 9(2), 125-147.
Warlaumont, A. S., Westermann, G., Buder, E. H., & Oller, D. K. (2013). Prespeech motor learning in a neural network using reinforcement. Neural Networks, 38, 64-75.
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological review, 114(2), 245.
Yu, C., & Ballard, D. H. (2007). A unified model of early word learning: Integrating statistical and social cues. Neurocomputing, 70(13), 2149-2165.
Simmering, V. R., Triesch, J., Deák, G. O., & Spencer, J. P. (2010). A dialogue on the role of computational modeling in developmental science. Child development perspectives, 4(2), 152-158.