Babybot : an artificial developing robotic agent

The study of development, either artificial or biological, can highlight the mechanisms underlying learning and adaptive behavior. We shall argue whether developmental studies might provide a different and potentially interesting perspective either on how to build an artificial adaptive agent, or on understanding how the brain solves sensory, motor, and cognitive tasks. It is our opinion that the acquisition of the proper behavior might indeed be facilitated because within an ecological context, the agent, its adaptive structure and the environment dynamically interact thus constraining the otherwise difficult " learning problem ". In very general terms we shall describe the proposed approach and supporting biological related facts. In order to further analyze these aspects from the modeling point of view, we shall demonstrate how a twelve degrees of freedom " baby " humanoid robot acquires orienting and reaching behaviors, and what kind of advantages the proposed framework might offer.

[1]  Giulio Sandini,et al.  An anthropomorphic retina-like structure for scene analysis , 1980 .

[2]  Zenon W. Pylyshyn,et al.  Computational processes in human vision : an interdisciplinary perspective , 1988 .

[3]  Dana H. Ballard,et al.  Principles of animate vision , 1992, CVGIP Image Underst..

[4]  James L. Crowley,et al.  Gaze Control for a Binocular Camera Head , 1992, ECCV.

[5]  D. O'Leary Development of connectional diversity and specificity in the mammalian brain by the pruning of collateral projections , 1992, Current Opinion in Neurobiology.

[6]  E. Thelen,et al.  The transition to reaching: mapping intention and intrinsic dynamics. , 1993, Child development.

[7]  F. A. Mussa-lvaldi,et al.  Convergent force fields organized in the frog's spinal cord , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  Giulio Sandini,et al.  A Binocular Active Vision System Using Space Variant Sensors: Exploiting Autonomous Behaviors for Sp , 1993 .

[9]  A. Streri Seeing, Reaching, Touching: The Relations Between Vision and Touch in Infancy , 1993 .

[10]  Matthew M. Williamson,et al.  Postural primitives: Interactive Behavior for a Humanoid Robot Arm , 1996 .

[11]  Yasuo Kuniyoshi,et al.  Learning of oculo-motor control: a prelude to robotic imitation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[12]  Pattie Maes,et al.  Postural primitives: Interactive Behavior for a Humanoid Robot Arm , 1996 .

[13]  J. Simonoff Multivariate Density Estimation , 1996 .

[14]  Rodney A. Brooks,et al.  Behavior-based humanoid robotics , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[15]  Rolf Pfeifer,et al.  Sensory - motor coordination: The metaphor and beyond , 1997, Robotics Auton. Syst..

[16]  Giulio Tononi,et al.  A robotic system emulating the adaptive orienting behavior of the barn owl , 1997, Proceedings of International Conference on Robotics and Automation.

[17]  Mitsuo Kawato,et al.  Human arm stiffness and equilibrium-point trajectory during multi-joint movement , 1997, Biological Cybernetics.

[18]  Giulio Sandini,et al.  Human Sensori-Motor Development and Artificial Systems , 1997 .

[19]  C. Hofsten,et al.  Development of smooth pursuit tracking in young infants , 1997, Vision Research.

[20]  T. Sejnowski,et al.  Irresistible environment meets immovable neurons , 1997, Behavioral and Brain Sciences.

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  R. Beer,et al.  Biorobotic approaches to the study of motor systems , 1998, Current Opinion in Neurobiology.

[23]  Giulio Sandini,et al.  Oculo-motor stabilization reflexes: integration of inertial and visual information , 1998, Neural Networks.

[24]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[25]  Giulio Sandini,et al.  A developmental approach to visually-guided reaching in artificial systems , 1999, Neural Networks.

[26]  Paul F. M. J. Verschure,et al.  What Can Robots Tell Us About Brains? A Synthetic Approach Towards the Study of Learning and Problem Solving , 1999, Reviews in the neurosciences.

[27]  Giulio Sandini,et al.  Learning VOR-like stabilization reflexes in robots , 2000, ESANN.

[28]  Gordon Cheng,et al.  Complex continuous meaningful humanoid interaction: a multi sensory-cue based approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[29]  Giulio Sandini,et al.  Visuo-inertial stabilization in space-variant binocular systems , 2000, Robotics Auton. Syst..