In this paper, we present goal-discovering robotic architecture for intrisically-motivated learning (GRAIL), a four-level architecture that is able to autonomously: 1) discover changes in the environment; 2) form representations of the goals corresponding to those changes; 3) select the goal to pursue on the basis of intrinsic motivations (IMs); 4) select suitable computational resources to achieve the selected goal; 5) monitor the achievement of the selected goal; and 6) self-generate a learning signal when the selected goal is successfully achieved. Building on previous research, GRAIL exploits the power of goals and competence-based IMs to autonomously explore the world and learn different skills that allow the robot to modify the environment. To highlight the features of GRAIL, we implement it in a simulated iCub robot and test the system in four different experimental scenarios where the agent has to perform reaching tasks within a 3-D environment.
GRAIL: a Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning
IEEE, Stati Uniti d'America
IEEE Transactions on Cognitive and Developmental Systems 8 (2016): 214–231. doi:10.1109/TCDS.2016.2538961
info:cnr-pdr/source/autori:Vieri Giuliano Santucci, Gianluca Baldassarre, Marco Mirolli/titolo:GRAIL: a Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning/doi:10.1109/TCDS.2016.2538961/rivista:IEEE Transactions on Cognitive and De