Autonomous learning based on depth perception and behavior generation

We propose a new neuro-robotic network that can simultaneously achieve a goal oriented behavior task and perception enhancement task for a visually-guided object manipulation based on learning by examples. The brain exploits action to develop perception qualities, and perceptual process helps to develop qualified-behavior. In order to import those action and perception inter-abilities of a brain into a humanoid robot, we consider two key inspirations: (1) Sensory Invariant Driven Action (SIDA) and (2) Object Size Invariance (OSI) characteristic. Considering robot manipulation of a target object with distance estimation as a perceptual process, we develop a new autonomous learning method based on the SIDA for behavior generation and OSI property for perceptual judgment. The proposed method is evaluated by using a humanoid robot (NAO) with stereo cameras, and the experimental results show that the proposed method is effective on autonomously improving the behavior generation performance as well as depth perception accuracy.

[1]  Karl Kral,et al.  Behavioural–analytical studies of the role of head movements in depth perception in insects, birds and mammals , 2003, Behavioural Processes.

[2]  Daniel Chern-Yeow Eng,et al.  Autonomous Learning of the Semantics of Internal Sensory States Based on Motor Exploration , 2007, Int. J. Humanoid Robotics.

[3]  Minho Lee,et al.  Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment , 2008, Neural Networks.

[4]  G. Rizzolatti,et al.  Action recognition in the premotor cortex. , 1996, Brain : a journal of neurology.

[5]  Mark Wexler,et al.  Depth perception by the active observer , 2005, Trends in Cognitive Sciences.

[6]  Minho Lee,et al.  Autonomous and Interactive Improvement of Binocular Visual Depth Estimation through Sensorimotor Interaction , 2013, IEEE Transactions on Autonomous Mental Development.

[7]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[8]  R. Held,et al.  MOVEMENT-PRODUCED STIMULATION IN THE DEVELOPMENT OF VISUALLY GUIDED BEHAVIOR. , 1963, Journal of comparative and physiological psychology.

[9]  Minho Lee,et al.  Biologically motivated vergence control system using human-like selective attention model , 2006, Neurocomputing.

[10]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[11]  R. Passingham,et al.  Action observation and acquired motor skills: an FMRI study with expert dancers. , 2005, Cerebral cortex.

[12]  A. S. Gilinsky.  Perceived size and distance in visual space. , 1951, Psychological review.

[13]  Karl Kral,et al.  Side-to-side head movements to obtain motion depth cues: A short review of research on the praying mantis , 1998, Behavioural Processes.

[14]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..

[15]  S. Chieffi,et al.  Visual illusion and action , 1996, Neuropsychologia.