This project aims to develop a new paradigm to build open-ended learning robots called "Goal-based Open ended Autonomous Learning" (GOAL).
GOAL rests upon two key insights. First, to exhibit an autonomous open-ended learning process, robots should be able to self-generate goals, and hence tasks to practice. Second, new learning algorithms can leverage self-generated goals to dramatically accelerate skill learning.
The new paradigm will allow robots to acquire a large repertoire of flexible skills in conditions unforeseeable at design time with little human intervention, and then to exploit these skills to efficiently solve new user-defined tasks with no/little additional learning.
More information can be found on the website www.goal-robots.eu.
Daniel Tanneberg, Jan Peters und Elmar Rueckert, Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks (2019), in: Neural Networks - Elsevier, 109(67-80)