In the Continuous Goal Directed Actions (CGDA) framework, actions are modelled as time series which contain the variations of object and environment features. As robot joint trajectories are not explicitly encoded in CGDA, Evolutionary Algorithms (EA) are used for the execution of these actions. These computations usually require a large number of evaluations. As a consequence of this, these evaluations are performed in a simulated environment, and the computed trajectory is then transferred to the physical robot.
In this paper, constraints are introduced in the CGDA framework, as a way to reduce the number of evaluations needed by the system to converge to the optimal robot joint trajectory. Specifically, spatial and velocity constraints are introduced in the framework. Their effects in two different CGDA commonly studied use cases (the "wax'' and "paint'' actions) are analyzed and compared. The experimental results obtained using these constraints are compared with those obtained with the Steady State Tournament (SST) algorithm used in the original proposal of CGDA. Conclusions extracted from this study depict a high reduction in the required number of evaluations when incorporating spatial constraints. Velocity constraints provide however less promising results, which will be discussed within the context of previous CGDA works.