  One of the most important challenges of Smart City Applications is to adapt
the system to interact with non-expert users. Robot imitation frameworks aim to
simplify and reduce times of robot programming by allowing users to program
directly through demonstrations. In classical frameworks, actions are modeled
using joint or Cartesian space trajectories. Other features, such as visual
ones, are not always well represented with these pure geometrical approaches.
Continuous Goal-Directed Actions (CGDA) is an alternative to these methods, as
it encodes actions as changes of any feature that can be extracted from the
environment. As a consequence of this, the robot joint trajectories for
execution must be fully computed to comply with this feature-agnostic encoding.
This is achieved using Evolutionary Algorithms (EA), which usually requires too
many evaluations to perform this evolution step in the actual robot. Current
strategies involve performing evaluations in a simulation, transferring the
final joint trajectory to the actual robot. Smart City applications involve
working in highly dynamic and complex environments, where having a precise
model is not always achievable. Our goal is to study the tractability of
performing these evaluations directly in a real-world scenario. Two different
approaches to reduce the number of evaluations using EA, are proposed and
compared. In the first approach, Particle Swarm Optimization (PSO)-based
methods have been studied and compared within CGDA: naive PSO, Fitness
Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO
(AFFG-PSO). The second approach studied the introduction of geometrical and
velocity constraints within CGDA. The effects of both approaches were analyzed
and compared in the wax and paint actions, two CGDA commonly studied use cases.
Results from this paper depict an important reduction in the number of
evaluations.
