Neural net robot The Intelli-Bot:
"An Ordinary Robot, An Extraordinary Mind"
By: Sanjay Aggarwal and Sahil Kapur
Introduction
Our project consisted of an elementary eight neuron network that used Hebbian Learning to train a robot to respond intelligently to input light stimuli.
First, we decided upon a task that would accurately denote Hebbian learning. One of the most common examples of conditional learning such as Hebbian learning is seen in Pavlov’s experiment with his dog. In this experiment, when food was offered to the dog, it caused the dog to salivate. At first the sound of a doorbell elicited no such response. However, Pavlov decided to sound the bell when he offered food to the dog. After a few repetitions of this experiment, the dog began to salivate at the sound of the bell even when no food was present. Here food was the unconditioned stimulus, and the doorbell was the conditioned stimulus. Similarly, in our experiment we first show that shining light in front of and or behind the robot elicits no response but pressing the push button causes the robot to move forward or backward. We then press the button while shining the light on the robot and the neural network programmed into the robot causes it to associate the light input with the push button input. Soon the robot moves forward or backward depending on whether the light is shined behind or in front of it in the absence of push button input. There are other neurons in this network that play an inhibitory role and prevent the robot from going too close to the light. They too display learning. Initially, the robot goes very close to a light source before it decides to move in the opposite direction. As time passes by the robot gets more responsive to the light and does not get too close to either light source.
In order to reach our end goal, we first programmed a three neuron neural network in C and thoroughly tested it using LEDs and hyperterm. We then extended this to a four neuron network and finally to an eight neuron network, with thorough testing at each level of complexity. Following this, we added the hardware interface. This involved integration of stepper motor control code into our neural network such that the stepper motors would step when the ‘motor’ neurons fired. We then built the chassis and added all the motors, LEDs, pushbuttons and MCU board to the design.
Link: http://instruct1.cit.cornell.edu/courses/e...SKA7/index.html
"An Ordinary Robot, An Extraordinary Mind"
By: Sanjay Aggarwal and Sahil Kapur
Introduction
Our project consisted of an elementary eight neuron network that used Hebbian Learning to train a robot to respond intelligently to input light stimuli.
First, we decided upon a task that would accurately denote Hebbian learning. One of the most common examples of conditional learning such as Hebbian learning is seen in Pavlov’s experiment with his dog. In this experiment, when food was offered to the dog, it caused the dog to salivate. At first the sound of a doorbell elicited no such response. However, Pavlov decided to sound the bell when he offered food to the dog. After a few repetitions of this experiment, the dog began to salivate at the sound of the bell even when no food was present. Here food was the unconditioned stimulus, and the doorbell was the conditioned stimulus. Similarly, in our experiment we first show that shining light in front of and or behind the robot elicits no response but pressing the push button causes the robot to move forward or backward. We then press the button while shining the light on the robot and the neural network programmed into the robot causes it to associate the light input with the push button input. Soon the robot moves forward or backward depending on whether the light is shined behind or in front of it in the absence of push button input. There are other neurons in this network that play an inhibitory role and prevent the robot from going too close to the light. They too display learning. Initially, the robot goes very close to a light source before it decides to move in the opposite direction. As time passes by the robot gets more responsive to the light and does not get too close to either light source.
In order to reach our end goal, we first programmed a three neuron neural network in C and thoroughly tested it using LEDs and hyperterm. We then extended this to a four neuron network and finally to an eight neuron network, with thorough testing at each level of complexity. Following this, we added the hardware interface. This involved integration of stepper motor control code into our neural network such that the stepper motors would step when the ‘motor’ neurons fired. We then built the chassis and added all the motors, LEDs, pushbuttons and MCU board to the design.
Link: http://instruct1.cit.cornell.edu/courses/e...SKA7/index.html