The future of
mankind is going to be affected in a very serious way by developments in
robotics. Let us make friends with robots and try to understand them.
There are two
main types of robots: industrial
robots, and autonomous robots.
Industrial robots do useful work in a structured or pre-determined environment.
They do repetitive jobs like fabricating cars, stitching shirts, or making
computer chips, all according to a set of instructions programmed into them.
Autonomous or
smart robots, by contrast, are expected to work in an unstructured environment. They move
around in an environment that has not been specifically engineered for
them, and do useful and ‘intelligent’ work. They have to interact with a
dynamically changing and complex world, with the help of sensors and actuators
and a brain centre.
There have
been several distinct or parallel approaches to the development of machine
intelligence (Nolfi and Floreano 2000). The
classical artificial-intelligence (AI) approach attempted to imitate some
aspects of rational thought. Cybernetics, on the other
hand, tended to adopt the human-nervous-system approach more directly. And evolutionary
or adaptive robotics embodies a convergence of the two approaches.
Thus the main
routes to the development of autonomous robots are:
- behaviour-based robotics;
- robot learning;
- artificial-life simulations (in conjunction with physical devices comprising the robot); and
- evolutionary robotics.
I have already
discussed artificial life in Part 77. Let us focus
on behaviour-based robotics here.
In the
traditional AI approach to robotics, the computational work for robot control
is decomposed into a chain of information-processing modules, proceeding from
overall sensing to overall final action. By contrast, in behaviour-based
robotics (Brooks; Arkin), the
designer provides the robot with a set of simple basic behaviours. A parallel
is drawn from how coherent intelligence (‘swarm intelligence’)
emerges in a beehive or an ant colony from a set of very simple behaviours. In such
a vivisystem, each agent is a simple device interacting with the world with
sensors, actuators, and a very simple brain.
In Brooks’ ‘subsumption architecture’,
the decomposition of the robot-control process is done in terms of behaviour-generating modules, each of
which connects sensing to action directly.
Like an individual bee in a beehive, each behaviour-generating module directly
generates some part of the behaviour of the robot. The tight (proximity)
coupling of sensing to action produces an intelligent network of simple
computational elements that are broad rather than deep in perception and action.
There are two
further concepts in this approach: ‘situatedness’,
and ‘embodiment’. Situatedness means the incorporation of the fact that the
robot is situated in the real world, which directly influences its sensing,
actuation, and learning processes. Embodiment means that the robot is not some
abstraction inside a computer, but has a body which must respond dynamically to
the signals impinging on it, using immediate feedback. This makes evolution of
intelligence in a robot more realistic than the artificial evolution carried
out entirely inside a computer.
In Brooks' (1986) approach, the
desired behaviour is broken down into a set of simpler behaviours (‘layers’),
and the solution (namely the control system) is built up incrementally, layer
by layer. Simple basic behaviours are mastered first, and behaviours of higher
levels of sophistication are added gradually, layer by layer. Although basic
behaviours are implemented in individual subparts or layers, a coordination
mechanism is incorporated in the control system, which determines the relative
strength of each behaviour in any particular situation.
Coordination
may involve both competition and cooperation. In a competitive scenario, only
one behaviour determines the motor output of the robot. Cooperation means that
a weighted sum of many behaviours determines the robot response.
In spite of
the progress made in behaviour-based robotics, the fact remains that autonomous
mobile robots are difficult to design. The reason is that their behaviour is an
emergent property (Nolfi and Floreano 2000). By their
very nature, emergent phenomena in a complex system (in this case the robot
interacting with its surroundings) are practically impossible to predict, even
if we have all the information about the sensor inputs to the robot and the
consequences of all the motor outputs. The major drawback of behaviour-based
robotics is that the trial-and-error process for improving performance is
judged and controlled by an outsider, namely the designer. It is not a fully
self-organizing and evolutionary approach for the growth of robotic
intelligence. And it is not easy for the designer to do a good job of breaking
down the global behaviour of a robot into a set of simple basic behaviours. One
reason for this difficulty is that an optimal solution of the problem depends
on who is describing the behaviour:
the designer or the robot? The description can be distal or proximal (Nolfi and Floreano 2000).
Proximal
description of the behaviour of the robot is a description from the vantage
point of the sensorimotor system that describes how the robot reacts to
different sensory situations.
The distal
description is from the point of view of the designer or the observer. In it,
the results of a sequence of sensorimotor loops may be described in terms of
high-level words like ‘approach’ or ‘discriminate’. Such a description of
behaviour is the result of not only the sensory-motor mapping, but also of the
description of the environment. It thus incorporates the dynamical interaction
between the robot and the environment, and that leads to some difficult
problems. The environment affects the robot, and the robot affects the
environment, which in turn affects the robot in a modified way, and so on. This
interactive loop makes it difficult for the designer to break up the global behaviour
of the robot into a set of elementary or basic behaviours that are simple from
the vantage point of the proximal description. Because of the emergent nature
of behaviour, it is difficult to predict what behaviour will result from a
given control system. Conversely, it is also difficult to predict what pattern
of control configurations will result in a desired behaviour.
As we shall
see in the next post, this problem is overcome in evolutionary robotics
by treating the robot and the environment as a single system, in which the
designer has no role to play. After all, this is how all complexity has evolved
in Nature.
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