The present dissertation addresses problems related to robot learning from demonstra¬
tion. It presents the building of a connectionist architecture, which provides the robot
with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬
guage taught by an external teacher agent. This thesis considers three main issues:
1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol
grounding of a robot's actions and perceptions, 3) development of a common symbolic
representation of the world by heterogeneous agents.
We build our approach on the assumption that grounding of symbolic communication
creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow
the agent to co-ordinate its actionn to that of the teacher agent, are required aside to
general cognitive abilities of associativity, in order to constrain the agent's attention
to making relevant perceptions, onto which it grounds the teacher agent's symbolic
expression. In addition, the agent should be provided with the cognitive capacity for
extracting spatial and temporal invariance in the continuous flow of its perceptions.
Based on this requirement, we develop a connectionist architecture for learning time
series. The model is a Dynamical Recurrent Associative Memory Architecture, called
DRAMA. It is a fully connected recurrent neural network using Hebbian update rules.
Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated
robotic experiments. Training of the network is computationally fast and inexpensive,
which allows its implementation for real time computation and on-line learning in a
inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark
recognition and prediction of perception-action sequence in maze travelling.
The architecture is applied to experiments on robot learning by imitation. A learner
robot is taught by a teacher agent, a human instructor and another robot, a vocabulary
to describe its perceptions and actions. The experiments are based on an imitative
strategy, whereby the learner robot reproduces the teacher's actions. While imitating
the teacher's movements, the learner robot makes similar proprio and exteroceptions
to those of the teacher. The learner robot grounds the teacher's words onto the set of
common perceptions they share. We carry out experiments in simulated and physical
environments, using different robotic set-ups, increasing gradually the complexity of
the task. In a first set of experiments, we study transmission of a vocabulary to
designate actions and perception of a robot. Further, we carry out simulation studies,
in which we investigate transmission and use of the vocabulary among a group of
robotic agents. In a third set of experiments, we investigate learning sequences of the
robot's perceptions, while wandering in a physically constrained environment. Finally,
we present the implementation of DRAMA in Robota, a doll-like robot, which can
imitate the arms and head movements of a human instructor. Through this imitative
game, Robota is taught to perform and label dance patterns. Further, Robota is taught
a basic language, including a lexicon and syntactical rules for the combination of words
of the lexicon, to describe its actions and perception of touch onto its body.