The research described in this thesis examines how machine learning mechanisms can be
used in an assembly robot system to improve the reliability of the system and reduce the
development workload, without reducing the flexibility of the system. The justification
foi' this is that for a robot to be performing effectively it is frequently necessary to
have gained experience of its performance under a particular configuration before that
configuration can be altered to produce a performance improvement. Machine learning
mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings.
• The establishment of the action-sensor couplings which are necessary for the
effective handling of uncertainty.
• Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the
coupling or the interaction of parameters and results, and from that model derive
an appropriate learning mechanism that will find a parametrisation for that model
that will enable good performance to be obtained. From this starting point it has
been possible to show how equal, or better performance can be obtained by using
iearning mechanisms which are neither derived from nor require a model of the task
being learned. Instead, by combining iteration and a task specific profit function it is
possible to use a generic behavioural module based on a learning mechanism to achieve
the task.Iteration and a task specific profit function can also be used to learn which behavioural
module from a pool of equally competent modules is the best at any one time to use
to achieve a particular goal. Like the other two kinds of learning, this successfully
automates an otherwise difficult test and evaluation process that would have to be
performed by a developer. In doing so effectively, it, like the other learning that has
been used here, shows that instead of being a peripheral issue to be introduced to
a working system, learning, carried out in the right way, can be instrumental in the
production of that working system
