1 / 38

Robotic experiments in Synthetic Psychology

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.

torin
Download Presentation

Robotic experiments in Synthetic Psychology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. KREST Institute Summer Research June 27, 2008 Robotic experiments in Synthetic Psychology

  2. Project Leader: Dr. Pedro Diaz-Gomez

  3. Research Group • Benoit Tufeu • Cora James • Judy Kula • Terri Godman

  4. 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

  5. Is this a robot?

  6. Problem: • To design a Braitenberg style robot simulation that would accurately touch a light and then seek the next.

  7. A Braitenberg Robot

  8. Trial and Error

  9. Four Robots Terri Benoit Judy Cora

  10. Four Environments Environment 1 Environment 2 Environment 4 Environment 3

  11. Four by Four sets of data

  12. Statistics on the Four Robots

  13. Dr. Diaz’s Analysis of Variables

  14. Parameters of the selected robot: Benoit’s Robot B +10 -2 + (variable)

  15. 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.

  16. 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.

  17. New 14 Light Test Environment

  18. Varying the Bias in Robot B – Training Environment 4

  19. Varying the Bias in Robot B – Test Environment 5

  20. 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.

  21. Distribution of Training Environment 4

  22. Distribution of Test Environment 5

  23. 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.

  24. Results of ANOVA test for training environment 4

  25. Results of ANOVA test for Test Environment 5

  26. 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.

  27. General Statistics on the Additional Experiments.

  28. K-S test results for Training Environment 4 Higher bias Lower bias

  29. K-S test results for Test Environment 5 Higher bias Lower bias

  30. 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.

  31. 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

  32. Application Building robots

  33. Building BYO-bots with Dr. Miller

  34. The Handy Board: the Brain and Power of the Robot

  35. Our First Working Robot

  36. Our First Robot in Action

  37. A New Prototype

  38. Thank You, Gracias, Merci, Dr. Diaz The End

More Related