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Neural oscillators offer simple and robust solutions to problems such as locomotion and dynamic manipulation. Unfortunately, the parameters of these systems are notoriously difficult to tune. This paper presents brief review of using artificial neural oscillators in different types of robots such as dish-spinning robots, fish-robots, giant swing robots etc. Open questions in this field are formulated.
The study presented in this article aims to improve our understanding of how people use zoomorphic robots in a health related setting in their domestic environments in general and, in particular, whether people are able to build (long- term) relationships with these robots. The influences of social and hedonic factors were examined, in addition to the normally studied utilitarian factors of the Technology...
Autonomous robotic systems and intelligent artificial agents' capability have advanced dramatically. Since the intelligent artificial agents have been developing more autonomous and human-like, the capability of them to make moral decisions becomes an important issue. In this work we developed an artificial neutral network which considered various effective factors for ethical assessment of an action...
An exoskeleton system is a compact, light-weight robotic mechanism that a human can put-on for the purpose of overcoming inadequate muscle strength during the performance of physical tasks. In doing so, this integrated human-machine system offers multiple opportunities for creating assistive technologies that can be used in biomedical, industrial, aerospace and everyday life applications. The scope...
Research on thruster fault diagnosis of Underwater Robots (URs) is undertaken to improve its whole system reliability. Based on the BP neural network, a recurrent neural network (RNN) is presented and the network training algorithm is deduced. The RNN is trained by voyage head and yaw turning experiments, and the well trained network is applied to model for the URs. Compared the outputs between model...
Supervised learning of voting automata for the surgeon's right hand motion recognition constitutes the main result reported in the present paper. Within the framework of the project, aiming the design of scrub nurse robot a number of methods for recognizing the current stage of the surgery has been developed. Obviously no one of the methods separately can guarantee hundred percent correct recognition...
We propose a machine learning method of a TV cameraman's shooting technique with a neural network so that a robot camera can automatically shoot like an experienced cameraman. An experiment using a simulator that we developed shows that, by using our method, the cameraman's shooting technique can be quickly and easily embodied in the control system of the robot camera. Moreover, we derive guidelines...
Visual servoing, using the visual measurements direct in the control loop, is a problem that in recent years has grown in interest. One of the main problems involved in these systems is that, while the robot manipulator has a well known model and identification methods have been available, the vision system introduces a nonlinear transformation and modifies the dynamics as seen in the image plane...
The following topics are dealt with: artificial neural networks; biodegradability prediction; cellular automata; evolutionary algorithms; swarm intelligence; emergent systems; artificial life; Lindenmayer systems; digital organisms; artificial immune systems; membrane computing; simulated annealing; communication networks and protocols; computing with words; common sense computing; cognitive modeling...
We present an innovative multi-robot communication idea of generic message interpretation system based on updated feed forward network (FFN). A message is passed using demonstration by robotic arm. Recurrent network model RNM is used to learn complex tasks' demonstration then simply learning action sequences; RNM comes with the limitations of time efficiency, storage and provides a rigid structure...
This letter considers the problem of mean-square exponential synchronization control for a class of stochastic delayed neural networks. Different from the prior works, the master-response synchronization setup under consideration transmits its signals through unreliable links, which include network-induced delays, frame losses, and random fluctuations. We firstly introduce a mathematical model of...
In this paper, we present an emotion recognition system using the stacked generalization ensemble neural network for special human affective state in the speech signal. 450 short emotional sentences with different contents from 3 speakers were collected as experiment materials. The features relevant with energy, speech rate, pitch and formant are extracted from speech signals. Stacked generalization...
Rat hippocampal neurons were cultured on a dish with 64 micro planer electrodes. We found that the silent and reproducible period lasting for 1 sec immediately after the activity evoked in advance. In addition, the repetitive stimuli suppress the spontaneously occurring bursting activity in frequency. These results suggest that distinct internal state of the neuronal circuit was triggered by an electrical...
Study of thruster fault diagnosis of Underwater Robots (URs) is undertaken to improve its whole system reliability. Based on the BP neural network, an improved recurrent neural network (RNN) is proposed and the network training algorithm is deduced. The RNN is trained by voyage head and yaw turning experiments, and the well trained network is applied to model for the URs. Compared the model's outputs...
Trajectory planning of robot is to control the robot in order to accurately follow the target track. And the target trajectory is always high-order and nonlinear. But RBF neural network can be achieved from the input to the output of arbitrary nonlinear mapping, through network learning and training to achieve the nonlinear function. This paper establishes a RBF neural network model firstly, and carries...
This paper presents an inverse optimal neural controller, which is constituted by the combination of two well known techniques: (a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and (b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to...
This paper describes an novel approach towards linguistic processing for robots through integration of a motion language module and a natural language module. The motion language module represents association between symbolized motion patterns and words. The natural language module models sentences. The motion language module and the natural language module are graphically integrated. The integration...
The this work deals with neural network-based gait-pattern adaptation algorithms for an active lower limbs orthosis. Stable trajectories are generated during the optimization process, considering a stable trajectory generator based on the Zero Moment Point criterion and the inverse dynamic model. Additionally, two neural network (NN) are used to decrease the time-consuming computation of the model...
In this paper, we propose an emotion generation system based on MaC model using neural networks. In the proposed system, the chaotic neural network and the Boltzmann machine are used in the emotion generator of the MaC model. In the Boltzmann machine, the plural pattern can be recalled stochastically for the same input. The proposed system makes use of this property in order to generate different...
To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate modes and different modes correspond to different hierarchical structure of the controller. This...
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