A. Lacaze et al., MULTIRESOLUTIONAL SCHEMATA FOR UNSUPERVISED LEARNING OF AUTONOMOUS ROBOTS FOR 3-D SPACE OPERATION, Robotics and computer-integrated manufacturing, 11(2), 1994, pp. 53-63
This paper describes a novel approach to the development of a learning
control system for autonomous space robot (ASR) that presents the ASR
as a ''baby''-that is, a system with no a priori knowledge of the wor
ld in which it operates, but with behavior acquisition techniques that
allow it to build this knowledge from the experiences of actions with
in a particular environment (we will call it an Astro-baby). The learn
ing techniques are rooted in the recursive algorithm for inductive gen
eration of nested schemata molded from processes of early cognitive de
velopment in humans. The algorithm extracts data from the environment,
and by means of correlation and abduction, it creates schemata that a
re used for control. This system is robust enough to deal with a const
antly changing environment because such changes provoke the creation o
f new schemata by generalizing from experiences, while still maintaini
ng minimal computational complexity, thanks to the system's multiresol
ution nature. Experimenting with ASR is especially interesting because
the rules of input control do not coincide with human intuitions. Act
ually, we want to see that the simulated device can learn unexpected s
chemata from its own experience. Although the traditional approach to
autonomous navigation involves off-line path planning with a known wor
ld map (such as the potential fields algorithm), in most of the real t
asks the environment is not well known because of ever-changing condit
ions such as absence of gravity and because of sophisticated, hard-to-
predict obstacles like components of the space station. Astro-baby gat
hers data from its sensors and then, by using a schema-discovery syste
m, it extracts concepts, forms schemata, and creates a quantitative/co
nceptual semantic network. When the Astro-baby is first dropped into s
pace, it does not have any experiences and its sensors and actuators a
re sets that do not have any distinction among elements. Then, by tria
l and error, the ASR learns the function of its actuators and sensors
and how to activate them to achieve the goal given by its creator or t
he sub-goals that it finds. In our simulation, the initial goal is to
minimize the distance to a beacon. The results of simulation are posit
ive: Astro-baby displays the ability to learn a number of maneuvers.