Learning from Approximate Human Decisions by a Robot

2007 ◽  
Vol 19 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Chandimal Jayawardena ◽  
◽  
Keigo Watanabe ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Natural Language ◽  
Knowledge Base ◽  
Probabilistic Neural Network ◽  
Robotic Manipulator ◽  
Human Aspects ◽  
Pick And Place ◽  
Robot Systems ◽  
Related Aspect

Robot systems operating under natural-language commands must be able to infer the meaning intended by the issuer. Despite some successful research, however, an important related aspect not yet addressed has been the possibility of learning from natural-language commands. Such commands, generated by human users, contain valuable information. The inherent subjectivity of natural language, however, complicates potential learning from such commands and their interpretation. We propose decision making for robots operating under natural-language commands influenced by human aspects of decision making. Under our proposed concept, demonstrated in experiments conducted using a robotic manipulator, the robot is controlled using natural-language commands to conduct pick-and-place operations, during which the robot builds a knowledge base. After this learning, which uses a probabilistic neural network, the robot conducts similar tasks based on approximate decisions from the knowledge gained.

Fuzzy Coach-Player Systems for Controlling a Robot Manipulator

2011 ◽  
pp. 229-251
Author(s):  
Chandimal Jayawardena ◽  
Keigo Watanabe ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Natural Language ◽  
Robot Manipulator ◽  
Probabilistic Neural Network ◽  
Decision Making Process ◽  
Human Beings ◽  
Redundant Manipulator ◽  
New Concepts ◽  
Human Decision

Natural language commands are information rich and conscious because they are generated by intelligent human beings. Therefore, if it is possible to learn from such commands and reuse that knowledge, it will be very effective and useful. In this chapter, learning from information rich voice commands for controlling a robot is discussed. First, new concepts of fuzzy coach-player system and sub-coach for robot control with natural language commands are proposed. Then, the characteristics of subjective human decision making process and learning from such decisions are discussed. Finally, an experiment conducted with a PA-10 redundant manipulator in order to establish the proposed concept is described. In the experiment, a Probabilistic Neural Network (PNN) is used for learning.

A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

2013 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Onsy A. Abdel Alim ◽  
Amin Shoukry ◽  
Neamat A. Elboughdadly ◽  
Gehan Abouelseoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Object Recognition ◽  
Probabilistic Neural Network ◽  
Training Procedure ◽  
Generalization Capability ◽  
Mine Detection ◽  
Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

EMG-Based Control of a Multi-Joint Robot for Operating a Glovebox

2015 ◽  
pp. 36-52 ◽  
Author(s):  
Toshio Tsuji ◽  
Taro Shibanoki ◽  
Keisuke Shima
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Probabilistic Neural Network ◽  
Robotic Manipulator ◽  
Control Command ◽  
Emg Patterns

This chapter describes a control method for a multi-joint robotic manipulator using Electromyogram (EMG) signals for operating a glovebox. The system uses a Probabilistic Neural Network (PNN) to estimate the user's intended motion from EMG patterns, and generates a control command for the glovebox and robotic manipulator corresponding to the estimated motions. The user can therefore control the manipulator as well as various functions of the glovebox system through his/her EMG signals while performing some manual operations through gloves. With this system, the authors produce intuitive control of the glovebox with the robotic manipulator. The authors confirm the effectiveness of the proposed system with an experiment using the developed prototype.

INGRESS: Interactive visual grounding of referring expressions

2020 ◽  
Vol 39 (2-3) ◽  
pp. 217-232 ◽  
Author(s):  
Mohit Shridhar ◽  
Dixant Mittal ◽  
David Hsu
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Referring Expressions ◽  
Question Generation ◽  
Input Language ◽  
Robot System ◽  
Everyday Objects ◽  
Second Stage ◽  
Object Categories ◽  
Pick And Place

This article presents INGRESS, a robot system that follows human natural language instructions to pick and place everyday objects. The key question here is to ground referring expressions: understand expressions about objects and their relationships from image and natural language inputs. INGRESS allows unconstrained object categories and rich language expressions. Further, it asks questions to clarify ambiguous referring expressions interactively. To achieve these, we take the approach of grounding by generation and propose a two-stage neural-network model for grounding. The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expressions, and identifies a set of candidate objects. The second stage uses another neural network to examine all pairwise relations between the candidates and infers the most likely referred objects. The same neural networks are used for both grounding and question generation for disambiguation. Experiments show that INGRESS outperformed a state-of-the-art method on the RefCOCO dataset and in robot experiments with humans. The INGRESS source code is available at https://github.com/MohitShridhar/ingress .

Development of knowledge oriented decision making support subsystem for intellectual information system

2020 ◽  
Vol 25 (1) ◽  
pp. 51-56
Author(s):  
Klymenko M.S. ◽  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Information System ◽  
Decision Support ◽  
Natural Language ◽  
Knowledge Base ◽  
The Common ◽  
The Creation ◽  
Intelligent Information ◽  
Rules Set

It is proposed to expand the structure of the intelligent information system with an addition of knowledge-oriented decision support subsystem. The description of an intellectual workplace is given. Based on this, the main procedures of the subsystem are proposed: the creation of a knowledge base and the search for appropriate responses to a given action. The structure and stages of creating a knowledge base based on the analysis of rules set in natural language are described. The advantages of this approach in comparison with the common approaches based on neural networks are substantiated.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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