Browsing by Author "Watanabe, K"

Now showing 1 - 13 of 13

Embargo
Approximate decision making by natural language commands for robots
(IEEE, 2006-11-06) Watanabe, K; Jayawardena, C; Izumi, K
Inferring the correct meaning of natural language commands, as judged by the person who issues commands, is mandatory for natural language commanded robotic systems. There have been some successful research on this; but one of the important and related aspects has not been addressed, i.e. the possibility of learning from natural language commands. Since natural language commands are generated by human users, they contain valuable information. Nevertheless, the learning from such commands, as well as the interpretation of them face many challenges due to the inherent subjectiveness of natural languages. In this paper, we propose a decision making process for natural language commanded robots which is influenced by certain characteristics of human decision making process. The proposed concept is demonstrated with an experiment conducted using a robotic manipulator. First, the robot is controlled with natural language commands to perform some pick and place operations during which the robot builds a knowledge base. After learning, the robot is capable of performing approximately similar tasks by making approximate decisions with the gained knowledge. For the decision making a probabilistic neural network is used
Embargo
Control of redundant manipulators by fuzzy linguistic commands
(IEEE, 2003-08-04) Pulasinghe, K; Watanabe, K; Izumi, K; Kiguchi, K
This paper presents a method of controlling redundant manipulator by spoken language commands consisting fuzzy linguistic information. The present system introduces the fuzzyneuro control paradigm to the contemporary speech controlled robotic systems, which are based on on-off control paradigm. The system is sensitive to the action activation commands, action modification commands, and action repetition commands of the human-robot conversation carried out by practical dialogues. C.redibility of the proposed system is experimentally proved by controlling a manipulator with seven degrees-of-freedom by fuzzy linguistic information enriched spoken language commands to perform an assembling task.
Open Access
Controlling a robot manipulator with fuzzy voice commands using a probabilistic neural network
(Springer-Verlag, 2006-07-06) Jayawardena, C; Watanabe, K; Izumi, K
Natural language commands are generated by intelligent human beings. As a result, they contain a lot of information. Therefore, if it is possible to learn from such commands and reuse that knowledge, it will be a very efficient process. In this paper, learning from such information rich voice commands for controlling a robot is studied. First, new concepts of fuzzy coachplayer system and sub-coach are proposed for controlling robots with natural language commands. Then, the characteristics of the subjective human decision making process are discussed and a Probabilistic Neural Network (PNN) based learning method is proposed to learn from such commands and to reuse the acquired knowledge. Finally, the proposed concept is demonstrated and confirmed with experiments conducted using a PA-10 redundant manipulator.
Open Access
A Fuzzy-Neural Network Based Human-Machine Interface for Voice Controlled Robots Trained by a Particle Swarm Optimization
(Korean Institute of Intelligent Systems, 2003-09-25) Watanabe, K; Chatterjee, A; Pulasinghe, K; Izumi, K; Kiguchi, K
Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.
Embargo
Intelligent interface using natural voice and vision for supporting the acquisition of robot behaviors
(IEEE, 2006-10-22) Watanabe, K; Jayawardena, C; Izumi, K
Natural language usage for robot control is essential for developing successful human-friendly robotic systems. In spite of the fact that the realization of robots with high cognitive capabilities that understand natural instructions as humans is quite difficult, there is a high potential for introducing voice interfaces for most of the existing robotic systems. Although there have been some interesting work in this domain, usually the scope and the efficiency of natural language controlled robots are limited due to constraints in the number of built in commands, the amount of information contained in a command, the reuse of excessive commands, etc. We present a multimodal interface for a robotic manipulator, which can learn both from human user voice instructions and vision input to overcome some of these drawbacks. Results of three experiments, i.e., learning situations, learning actions, and learning objects are presented.
Embargo
Modular fuzzy-neuro controller driven by spoken language commands
(IEEE, 2004-01-30) Pulasinghe, K; Watanabe, K; Izumi, K; Kiguchi, K
We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.
Embargo
A novel modular neuro-fuzzy controller driven by natural language commands
(IEEE, 2001-07-27) Watanabe, K; Pulasinghe, K; Kiguchi, K; Izumi, K
A method of interpreting imprecise natural language commands to machine understandable manner is presented in this paper. The proposed method tries to ease the process of man-machine interaction by combining the theoretical understanding of artificial neural networks and fuzzy logic. Both fields are very popular to mimic the human behavior in different research areas in artificial intelligence. The proposed system tries to understand the natural language command rather than mere recognition. The distinctive features of the artificial neural networks in pattern recognition and classification and the abilities of manipulating imprecise data by fuzzy systems are merged to recognize the machine sensitive words in the natural language command and then to interpret them to machine in machine identifiable manner. Modularity of the design tries to break up the complete task into manageable parts where the presence of individual part is vital to bridge the so-called man-machine gap.
Embargo
A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems
(IEEE, 2005-12-05) Chatterjee, A; Pulasinghe, K; Watanabe, K; Izumi, K
This paper shows the possible development of particle swarm optimization (PSO)-based fuzzy-neural networks (FNNs) that can be employed as an important building block in real robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs that can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by a user. The FNN is also trained to capture the user-spoken directive in the context of the present performance of the robot system. Hidden Markov model (HMM)-based automatic speech recognizers (ASRs) are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system has been successfully employed in two real-life situations, namely: 1) for navigation of a mobile robot; and 2) for motion control of a redundant manipulator.
Embargo
Posture control of robot manipulators with fuzzy voice commands using a fuzzy coach–player system
(Taylor & Francis Group, 2007-01-01) Jayawardena, C; Watanabe, K; Izumi, K
This paper presents a method of controlling robot manipulators with fuzzy voice commands. Recently, there has been some research on controlling robots using information-rich fuzzy voice commands such as 'go little slowly' and learning from such commands. However, the scope of all those works was limited to basic fuzzy voice motion commands. In this paper, we introduce a method of controlling the posture of a manipulator using complex fuzzy voice commands. A complex fuzzy voice command is composed of a set of fuzzy voice joint commands. Complex fuzzy voice commands can be used for complicated maneuvering of a manipulator, while fuzzy voice joint commands affect only a single joint. Once joint commands are learned, any complex command can be learned as a combination of some or all of them, so that, using the learned complex commands, a human user can control the manipulator in a complicated manner with natural language commands. Learning of complex commands is discussed in the framework of fuzzy coach–player model. The proposed idea is demonstrated with a PA-10 redundant manipulator.
Embargo
Teaching a tele-robot using natural language commands
(IEEE, 2005-11-07) Jayawardena, C; Watanabe, K; Izumi, K
For Internet-based teleoperation systems, user-friendly natural interfaces are advantageous because those systems are intended to be used by non-experts. In developing user friendly interfaces, natural language communication is mandatory. This work presents a system in which a sub-set of natural language is used to command a tele-robot manipulator doing an object sorting task. The paper discusses about referring to objects with natural language commands such as "pick the small red cube". This is achieved by learning individual lexical symbols that refer to colors, shapes, and sizes independently, and then inferring the meaning of a combination of them.
Open Access
Training of Fuzzy-Neural Network for Voice-Controlled Robot Systems by a Particle Swarm Optimization
(Institute of Control, Robotics and Systems, 2003-10-23) Watanabe, K; Chatterjee, A; Pulasinghe, K; Jin, S. O; Izumi, K; Kiguchi, K
The present paper shows the possible development of particle swarm optimization (PSO) based fuzzy-neural networks (FNN) which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs which can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by an user. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. Hidden Markov Model (HMM) based automatic speech recognizers are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system is successfully employed in a real life situation for motion control of a redundant manipulator.
Open Access
Voice Communication in Performing a Cooperative Task with a Robot
(Springer, Tokyo, 2002) Pulasinghe, K; Watanabe, K; Kiguchi, K; Izumi, K
This paper investigates the credibility of voice (especially natural language commands) as a communication medium in sharing advanced sensory capacity and knowledge of the human with a robot to perform a cooperative task. Identification of the machine sensitive words in the unconstrained speech signal and interpretation of the imprecise natural language commands for the machine has been considered. The system constituents include a hidden Markov model (HMM) based continuous automatie speech recognizer (ASR) to identify the lexical content of the user's speech signal, a fuzzy neural network (FNN) to comprehend the natural language (NL) contained in identified lexical content, an artificial neural network (ANN) to activate the desired functional ability, and contral modules to generate output signals to the actuators of the machine. The characteristic features have been tested experimentally by utilizing them to navigate a Khepera® in real time using the user's visual information transferred by speech signals
Open Access
Voice-controlled modular fuzzy neural controller with enhanced user autonomy
(Springer-Verlag, 2003-03) Pulasinghe, K; Watanabe, K; Kiguchi, K; Izumi, K
In this article, a fuzzy neural network (FNN)- based approach is presented to interpret imprecise natural language (NL) commands for controlling a machine. This system, (1) interprets fuzzy linguistic information in NL commands for machines, (2) introduces a methodology to implement the contextual meaning of NL commands, and (3) recognizes machine-sensitive words from the running utterances which consist of both in-vocabulary and out-ofvocabulary words. The system achieves these capabilities through a FNN, which is used to interpret fuzzy linguistic information, a hidden Markov model-based key-word spotting system, which is used to identify machine-sensitive words among unrestricted user utterances, and a possible framework to insert the contextual meaning of words into the knowledge base employed in the fuzzy reasoning process. The system is a complete system integration which converts imprecise NL command inputs into their corresponding output signals in order to control a machine. The performance of the system specifications is examined by navigating a mobile robot in real time by unconditional speech utterances.