Fady Alnajjar

A Spiking Neural Network with dynamic memory for a real autonomous mobile robot in dynamic environment

This work concerns practical issues surrounding the application of learning and memory in a real mobile robot towards optimal navigation in dynamic environments. A novel control system that contains two-level units (low-level and high-level) is developed and trained in a physical mobile robot ldquoe-Puckrdquo. In the low-level unit, the robotpsilas task is to navigate in a various local environments, by training N numbers of spiking neural networks (SNN) that have the property of spike time-dependent plasticity. All the trained SNNs are stored in a tree-type memory structure, which is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in new and previously trained environments. The memory is designed to hold memories of various lengths and has a simple searching mechanism. For controlling the memory size, forgetting and on-line dynamic clustering techniques are used. Experimental results have proved that the proposed model can provide a robot with learning and memorizing capabilities enable it to survive in complex and dynamic environments.

SELF-ORGANIZATION OF SPIKING NEURAL NETWORK THAT GENERATES AUTONOMOUS BEHAVIOR IN A REAL MOBILE ROBOT

In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.

Sensor-fusion in spiking neural network that generates autonomous behavior in real mobile robot

We here introduce a novel adaptive controller for autonomous mobile robot that binds N types of sensory information. For each sensory modality, sensory-motor connection is made by a three-layered spiking neural network (SNN). The synaptic weights in the model have the property of spike timing-dependent plasticity (STDP) and regulated by presynaptic modulation signal from the sensory neurons. Each synaptic weight is incrementally adapted depending upon the firing rate of the presynaptic modulation signal and that of the hidden-layer neuron(s). Information from different types of sensors are bound at the motor neurons. A real mobile robot Khepera with the SNN controller quickly adapted into an open environment and performed the desired task successfully. This approach could be applicable to a robot with inputs of various sensory modalities and various types of motor outputs.

A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots

In this article, we describe an adaptive controller for an autonomous mobile robot with a simple structure. Sensorimotor connections were made using a three-layered spiking neural network (SNN) with only one hidden-layer neuron and synapses with spike timing-dependent plasticity (STDP). In the SNN controller, synapses from the hidden-layer neuron to the motor neurons received presynaptic modulation signals from sensory neurons, a mechanism similar to that of the withdrawal reflex circuit of the sea slug, Aplysia. The synaptic weights were modified dependent on the firing rates of the presynaptic modulation signal and that of the hidden-layer neuron by STDP. In experiments using a real robot, which uses a similar simple SNN controller, the robot adapted quickly to the given environment in a single trial by organizing the weights, acquired navigation and obstacle-avoidance behavior. In addition, it followed dynamical changes in the environment. This associative learning scheme can be a new strategy for constructing adaptive agents with minimal structures, and may be utilized as an essential mechanism of an SNN ensemble that binds multiple sensory inputs and generates multiple motor outputs.

Self-Organization of Spiking Neural Network Generating Autonomous Behavior in a Real Mobile Robot

In this paper, we study the relation between neural dynamics and robot behavior to develop self-organization algorithm of spiking neural network applicable to autonomous robot. We first formulated a spiking neural network model whose inputs and outputs were analog. We then implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task exists with the spiking neural network, the robot was evolved with the genetic algorithm (GA) in an environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. Then, a self-organization algorithm based on the use-dependent synaptic potentiation and depotentiation was formulated and implemented into the robot. In the environment, the robot gradually organized the network and the obstacle avoidance behavior was formed. The time needed for the training was much less than with genetic evolution, approximately one fifth (1/5)

A Hierarchical Autonomous Robot Controller for Learning and Memory: Adaptation in a Dynamic Environment

This work concerns practical issues surrounding the application of learning and memory in a real mobile robot with the goal of optimal navigation in dynamic environments. A novel hierarchical adaptive controller that contains two-level units was developed and trained in a physical mobile robot “e-Puck.” In the low-level unit, the robot holds a number of biologically inspired Aplysia -like spiking neural networks that have the property of spike time-dependent plasticity. Each of these networks is trained to become an expert in a particular local environment(s). All the trained networks are stored in a tree-type memory structure that is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in both new and previously trained environments. The robots memory is designed to hold memories of various lengths and has a simple searching mechanism. Forgetting and dynamic clustering techniques are used to control the memory size. Experimental results show that the proposed model can produce a robot with learning and memorizing capabilities that enable it to survive in complex and highly dynamic environments.

Use-dependent Synaptic Connection Modification in SNN Generating Autonomous Behavior in a Khepera Mobile Robot

In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot. We also examine the relation between neural dynamics in SNN and the robot behavior. First, we formulated a SNN model whose inputs and outputs were analog. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use dependent synaptic potentiation and depotentiation was formulated and implemented into the robot. In the environment, the robot gradually organized the network and the given tasks were successfully performed. The time needed for the training using self-organization method was much less than with genetic evolution, approximately one fifth

A New Dynamic Edge Detection toward Better Human-Robot Interaction

Robots vision plays a significant role in human-robot interaction, e.g., face recognition, expression understanding, motion tracking, etc. Building a strong vision system for the robot, therefore, is one of the fundamental issues behind the success of such an interaction. Edge detection, which is known as the basic units for measuring the strength of any vision system, has recently been taken attention from many groups of robotic researchers. Most of the reported works surrounding this issue have been based on designing a static mask, which sequentially move through the pixels in the image to extract edges. Despite the success of these works, such statically could restrict the models performance in some domains. Designing a dynamic mask by the inspiration from the basic principle of retina, and which supported by a unique distribution of photoreceptor, therefore, could overcome this problem. A human-like robot (RobovieR-2) has been used to examine the validity of the proposed model. The experimental results show the validity of the model, and it is ability to offer a number of advantages to the robot, such as: accurate edge detection and better attention to the front user, which is a step towards human-robot interaction.

Vision-sensorimotor abstraction and imagination towards exploring robot’s inner world

Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing them with its internal simulation. The accuracy of this hypothesis is strongly based on the agentpsilas experiences. As much as the agent knows about the environment, as much as it can build a strong internal representation about it. Although many works have been presented regarding to this hypothesis with various levels of success. At the sensorimotor abstraction level, where extracting data from the environment occur, however, none of them have so far used the robotpsilas vision as a sensory input. In this study, vision-sensorimotor abstraction is presented through memory-based learning in a real mobile robot ldquoHemissonrdquo to investigate the possibilities of explaining its inner world based on internal simulation of perception and action at the abstract level. The analysis of the experiments illustrate that our robot with vision sensory input has developed some kind of simple associations or anticipation mechanism through interacting with the environment, which enables, based on its history and the present situation, to guide its behavior in the absence of any external interaction.

A Novel Bioinspired Vision System: A Step toward Real-Time Human-Robot Interactions

Building a human-like robot that could be involved in our daily lives is a dream of many scientists. Achieving a sophisticated robots vision system, which can enhance the robots real-time interaction ability with the human, is one of the main keys toward realizing such an autonomous robot. In this work, we are suggesting a bioinspired vision system that helps to develop an advanced human-robot interaction in an autonomous humanoid robot. First, we enhance the robots vision accuracy online by applying a novel dynamic edge detection algorithm abstracted from the rules that the horizontal cells play in the mammalian retina. Second, in order to support the first algorithm, we improve the robots tracking ability by designing a variant photoreceptors distribution corresponding to what exists in the human vision system. The experimental results verified the validity of the model. The robot could have a clear vision in real time and build a mental map that assisted it to be aware of the frontal users and to develop a positive interaction with them.