RESILIENT, SELF-MODELING ROBOTS |
of Alexander Gloye-Förster et al. (2005)
In 2004, RoboCup world champion
(presently at IDSIA)
and his team built the first
resilient machines using continuous self-modeling.
Their robots can autonomously recover from
certain types of unexpected damage, through adaptive self-models
derived from actuation-sensation relationships, used to generate
forward locomotion. References are Gloye's PhD thesis as well as:
Gloye, A., Wiesel, F., Tenchio, O., Simon, M. Reinforcing the Driving
Quality of Soccer Playing Robots by Anticipation, IT - Information
Technology, vol. 47, nr. 5, Oldenbourg Wissenschaftsverlag, 2005.
Right: One of Gloye-Förster's
FU-Fighters that won the 2004 RoboCup
in the fast league (where human adversaries with a joystick have no chance).
The machine continually uses an artificial neural net to model current
properties of its 4 wheel omnidirectional drive.
Below: scene from the 2004 final.
Possibly the second publication about a resilient robot of this type was:
Resilient Machines Through Continuous Self-Modeling, by
J. Bongard, V. Zykov, H. Lipson, Science, 314: 1118-1121, 2006;
with a comment by C. Adami, "What Do Robots Dream Of?", 1093-1094.
(As pointed out in Science (2007), the reviewers
did not realize
this had been done before.)
a damaged RoboCup robot is no longer able to execute a precise star-shaped
Below: The same robot quickly adapts its neural model of the
relation between motor commands and sensory inputs, and uses it
successfully to plan and optimize its driving
trajectories, in the spirit of a paper by
An on-line algorithm for dynamic reinforcement learning and planning
in reactive environments.
In Proc. IEEE/INNS IJCNN, San Diego, vol. 2, p. 253-258, 1990.
Essentially the robot heals itself,
becoming resilient through continuous
Here are some relevant excerpts from the 2005 article by Gloye et al.:
"The same techniques applied in the previous section to robots
which are not accurate enough can now be applied to solve the
problem of a damaged motor. [...]
Our robots have four omnidirectional wheels, when one
motor is damaged, the robot has enough redundancy to still drive
omnidirectionally but the PID controller on the robot tries to
control four motors. We could of course have different PID
controllers in the robot, and when a motor fails, we could switch
from a four wheel to a three wheel controller. However, if the
motor just partially fails (it starts to deliver less power, if
for example the motor has become very hot) it would be desirable
to have a way of adapting the commands sent by the high-level
control. Also, the robot electronics could be a black-box which we
do not want or cannot modify.
In our experiments, we took a robot with four motors and
disconnected one of them.
The vision system tracks the robot for
some time and learns to predict its response function to commands,
as discussed above. We then apply the on-line correction to the
damaged robot with great success.
As can be seen [...] the driving behavior of the damaged
robot is similar to that of a fault-free robot. The robot is
somewhat slower, but it can drive accurately again.
As this simple experiment shows, it is then feasible to make these
types of corrections during RoboCup games. If a motor completely
fails, or loses power, the high-level control can let the robot
drive for some time, relearn its driving behavior, and apply the
online correction. The result is a robot that heals after a few
seconds because the coach (the central computer) knows which
commands to send ..."
Check out a brief letter on this:
J. Schmidhuber: Prototype resilient, self-modeling robots. Science 316, no. 5825 p 688, May 2007