29 May 2005

Tables for paper: Prince, Helder, & Hollich (2005), Ongoing emergence: A core concept in epigenetic robotics. EpiRob05.
(http://www.cprince.com/PubRes/EpiRob05).

Table 1: Criteria for Assessing Robotic Ongoing Emergence

Criterion

Description
1. New skill creation An agent creates new skills by utilizing its current environmental resources, internal state, physical resources, and by integrating current skills from the agents repertoire.
2. Incorporation of new skills with existing skills These new skills are incorporated into the agents' skill repertoire and form the basis from which further potential development can proceed.
3. Autonomous development of motivations In a manner similar to its development of skills in Criterion 1 and 2, the robot develops its values and goals.
4. Bootstrapping of new skills When the system starts, some skills rapidly become available.
5. Stability of skills Skills persist over an interval of time.
6. Reproducibility The same system started in similar initial states in a similar environment also displays similar ongoing emergence.

Table 2: Emergent Skills in Epigenetic Robots

Citation

Emergent skill

Mechanisms

Dominguez & Jacobs (2003)

Improvement in binocular disparity sensitivity

Developmental progressions of visual acuity; 1 dimensional visual images; connectionist model

French et al. (2002). Improvements in basic-level category differentiation Reduced visual acuity inputs; connectionist model

Lungarella & Berthouze (2002)

Berthouze & Lungarella (2004)

Swinging behavior in a small-scale humanoid robot

Staging release of degrees of freedom; neural oscillators; freezing and freeing degrees of freedom

Berthouze & Kuniyoshi (1998)

Visual tracking of moving objects

Independent adaptive controllers interacting through a robot body

Metta, Sandini, & Konczak (1999)

Accurate target-oriented reaching

Inaccurate reaching reflex; accurate visual target fixation; learning reaches that correspond to visual targets

Nagai, Hosoda, Morita, & Asada (2003)

Tracking face view to objects

Face & color detection; turning robot head to view colored object; learning motor command to change from face view to a salient object view

Lovett & Scassellati (2004)

Perceptual object permanence

Habituation to the relative location and depth of visual elements

Chen & Weng (2004)

Perceptual object permanence

IHDR learning (Weng & Hwang, 2003); Novelty, based on differences between visual predictions and actual sensations

Seth et al. (2004)

Visual object discrimination

Phase and firing rate neural model; feedback connectivity within and between neural regions; synchronously active regions

Kaplan & Oudeyer (2003)

Visual tracking

Predictability, stability, and familiarity variables

REFERENCES

Berthouze, L. & Kuniyoshi, Y. (1998). Emergence and categorization of coordinated visual behavior through embodied interaction. Machine Learning , 31 , 187-200.

Berthouze, L., & Lungarella, M. (2004). Motor skill acquisition under environmental perturbations: On the necessity of alternate freezing and freeing of degrees of freedom. Adaptive Behavior , 12 , 47-63.

Chen, Y. & Weng, J. (2004). Developmental learning: A case study in understanding “object permanence.” In Proceedings of the Fourth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems . Lund, Sweden: Lund University Cognitive Studies.

Dominguez, M. & Jacobs, R. A. (2003). Developmental constraints aid the acquisition of binocular disparity sensitivities. Neural Computation , 15 , 161-182.

French, R. M., Mermillod, M., Quinn, P. C., Chauvin, A., & Mareschal, D. (2002). The importance of starting blurry: Simulating improved basic-level category learning in infants due to weak visual acuity. Proceedings of the 24th Annual Conference of the Cognitive Science Society (pp. 322-327). New Jersey: LEA.

Kaplan, F. & Oudeyer, P-Y. (2003). Motivational principles for visual know-how development. In Proceedings of the 3rd International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems . Lund University Cognitive Studies.

Lovett, A., & Scassellati, B. (2004). Using a robot to reexamine looking time experiments. In Proceedings of the Third International Conference on Development and Learning (ICDL 04).

Lungarella, M., & Berthouze, L. (2002). On the interplay between morphological, neural and environmental dynamics: A robotic case-study. Adaptive Behavior , 10 , 223-241.

Metta, G., Sandini, G., & Konczak, J. (1999). A developmental approach to visually-guided reaching in artificial systems. Neural Networks , 12 , 1413-1427.

Nagai, Y., Hosoda, K., Morita, A., & Asada, M. (2003). A constructive model for the development of joint attention. Connection Science , 15 , 211-229.

Seth, A. K., McKinstry, J. L., Edelman, G. M., & Krichmar, J. L. (2004). Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device. Cerebral Cortex , 14 , 1185-1199.

Weng, J. & Hwang, W. (2003). Online image classification using IHDR. International Journal on Document Analysis and Recognition , 5 , 118-125.