Biological Cybernetics
Singularity Theory and Phantom Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kinematic networks distributed model for representing and regularizing motor redundancy
Biological Cybernetics
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Pattern Recognition
Height and gradient from shading
International Journal of Computer Vision
On sequential shape descriptions
Pattern Recognition
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Locating Perceptually Salient Points on Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Shape Analysis Model with Applications to a Character Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Perceptual Model of Handwriting Drawing Application to the Handwriting Segmentation Problem
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Imitation in animals and artifacts
Imitation in animals and artifacts
The shape of handwritten characters
Pattern Recognition Letters
Online Handwriting Recognition for Tamil
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
PATRAM " A Handwritten Word Processor for Indian Languages
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Reaching with multi-referential dynamical systems
Autonomous Robots
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A neural mechanism of synergy formation for whole body reaching
Biological Cybernetics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Shape: Talking about Seeing and Doing
Shape: Talking about Seeing and Doing
Active Information Selection: Visual Attention Through the Hands
IEEE Transactions on Autonomous Mental Development
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing
International Journal of Automation and Computing
Hi-index | 0.00 |
The core cognitive ability to perceive and synthesize `shapes' underlies all our basic interactions with the world, be it shaping one's fingers to grasp a ball or shaping one's body while imitating a dance. In this article, we describe our attempts to understand this multifaceted problem by creating a primitive shape perception/synthesis system for the baby humanoid iCub. We specifically deal with the scenario of iCub gradually learning to draw or scribble shapes of gradually increasing complexity, after observing a demonstration by a teacher, by using a series of self evaluations of its performance. Learning to imitate a demonstrated human movement (specifically, visually observed end-effector trajectories of a teacher) can be considered as a special case of the proposed computational machinery. This architecture is based on a loop of transformations that express the embodiment of the mechanism but, at the same time, are characterized by scale invariance and motor equivalence. The following transformations are integrated in the loop: (a) Characterizing in a compact, abstract way the `shape' of a demonstrated trajectory using a finite set of critical points, derived using catastrophe theory: Abstract Visual Program (AVP); (b) Transforming the AVP into a Concrete Motor Goal (CMG) in iCub's egocentric space; (c) Learning to synthesize a continuous virtual trajectory similar to the demonstrated shape using the discrete set of critical points defined in CMG; (d) Using the virtual trajectory as an attractor for iCub's internal body model, implemented by the Passive Motion Paradigm which includes a forward and an inverse motor model; (e) Forming an Abstract Motor Program (AMP) by deriving the `shape' of the self generated movement (forward model output) using the same technique employed for creating the AVP; (f) Comparing the AVP and AMP in order to generate an internal performance score and hence closing the learning loop. The resulting computational framework further combines three crucial streams of learning: (1) motor babbling (self exploration), (2) imitative action learning (social interaction) and (3) mental simulation, to give rise to sensorimotor knowledge that is endowed with seamless compositionality, generalization capability and body-effectors/task independence. The robustness of the computational architecture is demonstrated by means of several experimental trials of gradually increasing complexity using a state of the art humanoid platform.