Development of writing task recombination technology based on DMP segmentation via verbal command for Baxter robot

This paper developed a character recombination technology based on dynamic movement primitive (DMP) segmentation using verbal command on a Baxter robot platform. Movements are recorded from a human demonstrator. The operator physically guides the Baxter robot to perform the movements for five times. This training data set is also utilized for playback process. Subsequently, the dynamic time warping is employed to pre-treat the data. The DMP is used to model and generalize every single movement. Gaussian mixture model is used to generate multiple patterns after the teaching process. Then the Gaussian mixture regression algorithm is applied to reduce the position errors in 3D space after the generation of a synthesized trajectory. A remote PC is used to control the command of Baxter to record or playback any trajectories via user datagram protocol (UDP) by typing commands in a text file. In addition, Dragon NaturalSpeaking software is used to transfer the voice data to text data. This proposed approach is tested by performing a Chinese character writing task with a Baxter robot, where different Chinese characters are written by teaching only one character

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

An Arabic Optical Braille Recognition System

Technology has shown great promise in providing access to textual information for visually impaired people. Optical Braille Recognition (OBR) allows people with visual impairments to read volumes of typewritten documents with the help of flatbed scanners and OBR software. This project looks at developing a system to recognize an image of embossed Arabic Braille and then convert it to text. It particularly aims to build fully functional Optical Arabic Braille Recognition system. It has two main tasks, first is to recognize printed Braille cells, and second is to convert them to regular text. Converting Braille to text is not simply a one to one mapping, because one cell may represent one symbol (alphabet letter, digit, or special character), two or more symbols, or part of a symbol. Moreover, multiple cells may represent a single symbol