  Continuous Goal-Directed Actions (CGDA) is a robot imitation framework that
encodes actions as the changes they produce on the environment. While it
presents numerous advantages with respect to other robot imitation frameworks
in terms of generalization and portability, final robot joint trajectories for
the execution of actions are not necessarily encoded within the model. This is
studied as an optimization problem, and the solution is computed through
evolutionary algorithms in simulated environments. Evolutionary algorithms
require a large number of evaluations, which had made the use of these
algorithms in real world applications very challenging. This paper presents
online evolutionary strategies, as a change of paradigm within CGDA execution.
Online evolutionary strategies shift and merge motor execution into the
planning loop. A concrete online evolutionary strategy, Online Evolved
Trajectories (OET), is presented. OET drastically reduces computational times
between motor executions, and enables working in real world dynamic
environments and/or with human collaboration. Its performance has been measured
against Full Trajectory Evolution (FTE) and Incrementally Evolved Trajectories
(IET), obtaining the best overall results. Experimental evaluations are
performed on the TEO full-sized humanoid robot with "paint" and "iron" actions
that together involve vision, kinesthetic and force features.
