Effect Regulated Projection of Robot's Action Space for Production and Prediction of Manipulation Primitives Through Learning Progress and Predictability-Based Exploration

In this article, we propose an effective action parameter exploration mechanism that enables efficient discovery of robot actions through interacting with objects in a simulated table-top environment. For this, the robot organizes its action parameter space based on the generated effects in the environment and learns forward models for predicting consequences of its actions. Following the intrinsic motivation approach, the robot samples the action parameters from the regions that are expected to yield high learning progress (LP). In addition to the LP-based action sampling, our method uses a novel parameter space organization scheme to form regions that naturally correspond to qualitatively different action classes, which might be also called action primitives. The proposed method enabled the robot to discover a number of lateralized movement primitives and to acquire the capability of predicting the consequences of these primitives. Furthermore, our results suggest the reasons behind the earlier development of grasp compared to push action in infants. Finally, our findings show some parallels with data from infant development where correspondence between action production and prediction is observed.