Towards Visually-Guided Neuromorphic Robots



Beobots in Action

We are developing a number of neuromorphic visually-guided behaviors for the Beobot. Please see our publication page for details.

One important aspect of our research is in learning to navigate. To this end, we feed decoded signals from the radio control of the robot into the sound card input of one of the motherboards. This allows the robot to measure a teaching signal sent by human operators helping it in its learning process.

Testing under human control

Here we tested the mechanical stability of the robot under human radio control.

Learning to navigate

Christopher Ackerman developed a nifty piece of software, by which the robot learns to navigate based on a global analysis of the gist of the scene it perceives through its video camera.

The interesting aspect of the approach used here is that it makes no assumption about the contents or structure of the images seen by the Beobot. Instead, a generic analysis of the image is performed (using a Fourier transform), converting each frame into a 40-number 'signature'. A two-layer backprop network then learns the appropriate actions to take for given signatures. As an example, let's train the Beobot to navigate along a different path, and under very different lighting conditions (dusk).

  • Let's train for a bit (21 MB DivX). Here Chris corrects the Beobot as it makes mistakes while learning about this new (never seen before) environment.
  • Much better! (18 MB DivX). After just one run along the path from start to finish, now the Beobot is already making much fewer mistakes while driving back along the path, from finish to start (although it is the first time the Beobot is driving along this path in this direction).
  • Autonomous mode (23 MB DivX). The Beobot now is fairly comfortable driving along the path almost entirely on its own.
  • Mission accomplished (14 MB DivX). The Beobot is now comfortable at navigating in this difficult environment.

Copyright © 2005 by the University of Southern California, iLab and The Beobot Team. Last updated Thursday, 02-Sep-2010 10:05:32 PDT