In this paper, we study how human-robot interaction can be beneficial on the Continuous Goal-Directed Actions (CGDA) framework.
Specifically, a system for robot discovery of motor primitives from random human-guided movements has been developed. These guided motor primitives (GMP) are used as scaffolds to reproduce a goal-directed actions. CGDA encodes goals as the changes produced on object features (color, area, etc) due to actions. This paper focuses on using motor primitives extracted from human-guided random robot movements to execute these goal-directed actions. The human guides the robot joints in random movements, which are later divided in small segments. These segments are compared in terms of joint positions and selected to be diverse. To perform goal-directed actions, the robot must discover an adequate sequence of GMP. To discover these sequences we organize the primitives as a tree with incremental depths (where each node represents a primitive) and use a breadth-first search. In one of the experiments performed, the robot executes a task based on spatial object features. In the other experiment, the goal is to paint a wall by following a color feature trajectory.