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KREST Institute Summer Research June 27, 2008. Robotic experiments in Synthetic Psychology. Project Leader: Dr. Pedro Diaz-Gomez. Research Group. Benoit Tufeu Cora James Judy Kula Terri Godman. First: What is a Robot?. It is an autonomous system. It must exist in the physical world. - PowerPoint PPT Presentation
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ROBOTIC EXPERIMENTS IN
SYNTHETIC PSYCHOLOGY
KREST Institute Summer Research
June 27, 2008
Project Leader: Dr. Pedro Diaz-Gomez
Research Group
Benoit Tufeu Cora James Judy Kula Terri Godman
First: What is a Robot?
It is an autonomous system. It must exist in the physical world. It must be able to sense its environment. It can act based on the sensor. It must achieve a goal.
Pg. 2 The Robotics Primer, Maja J. Matarić
Is this a robot?
Problem:
To design a Braitenberg style robot simulation that would accurately touch a light and then seek the next.
A Braitenberg Robot
Trial and Error
Four Robots
TerriBenoit
JudyCora
Four Environments
Environment 1Environment 2Environment 3Environment 4
Four by Four sets of data
Statistics on the Four Robots
Dr. Diaz’s Analysis of Variables
Parameters of the selected robot: Benoit’s Robot B
+10
-2
+ (variable)
Hypothesis
A robot simulation that includes a central sensor with a small positive bias will be more accurate in acquiring and hitting lights then one with a central sensor with a high positive bias.
Note: Bias is a value that controls the speed of the engine based on the intensity of the light.
Experiments
Run Robot B through training Environment 4 with 10 trials, varying the bias on the central sensor from 0.0 to 1.0 (in 0.1 intervals).
Run Robot B through a new 14 light test environment with 10 trials, varying the bias in the same manner.
New 14 Light Test Environment
Varying the Bias in Robot B – Training Environment 4
Varying the Bias in Robot B – Test Environment 5
Initial Evaluations
After reviewing the data, we found that there was little difference in the accuracy at low biases of 0 to 1.
Further tests were run at biases of 5, 10, 15, and 20 in order to have a wider range of data.
Distribution of Training Environment 4
Distribution of Test Environment 5
Results of ANOVA test
All data from biases 0 to 20 on the central sensor was compared to the number of lights hit.
This showed statistically that bias has an effect on accuracy.
Results of ANOVA test for training environment 4
Results of ANOVA test for Test Environment 5
Analysis of ANOVA tests
The very small P-values statistically show that the bias value of the central sensor has a significant effect on the accuracy of robot performance.
To support our hypothesis that a low bias is more accurate, a KS (Kolmogorov-Smirnov) test was run to compare biases under one to biases greater than one.
General Statistics on the Additional Experiments.
Training Environment 4Bias 0.0 to 0.9
Training Environment 4Bias 1.0 to 20.0
TestEnvironment 5Bias 0.0 to 0.9
TestEnvironment 5Bias 1.0 to 20.0
Mean 7.185 3.640 11.67 10.08
Standard deviation
1.77 2.45 .958 2.17
P value 0.000 0.000 0.000 0.000
1st Quartile 8.00 2.00 12.0 8.00
2nd Quartile 8.00 3.00 12.0 11.00
3rd Quartile 8.00 4.75 12.0 12.0
Maximum 8.00 8.00 12.0 13.0
Minimum 2.00 0.00 12.0 6.00
K-S test results for Training Environment 4
Higher bias
Lower bias
K-S test results for Test Environment 5
Higher bias
Lower bias
Conclusions
Based on the results of the KS tests for both environments, a bias below 1 on the central sensor is more accurate then a bias above 1. This is consistent with our hypothesis.
Future Research Could Include
a larger trial population in order to be statistically more significant.
more test environments. more variation of biases. investigations on biases of the other
sensors. changes in the positions of sensors. programming that allows for evaluation
of environmental conditions
BUILDING ROBOTSApplication
Building BYO-bots with Dr. Miller
The Handy Board: the Brain and Power of the Robot
Our First Working Robot
Our First Robot in Action
A New Prototype
THE ENDThank You,
Gracias, Merci,
Dr. Diaz