Biography
Abstract
Open-ended learning, also named `life-long learning’, `autonomous curriculum learning’, `no-task learning’) aims to build learning machines and robots that are able to acquire skills and knowledge in an incremental fashion. The REAL competition, which is part of NeurIPS 2019 competition track, addresses open-ended
learning with a focus on `Robot open-Ended Autonomous Learning’ (REAL), that is on systems that:
(a) acquire sensorimotor competence that allows them to interact with objects and physical
environments;
(b) learn in a fully autonomous way, i.e. with no human intervention, on the basis of mechanisms such
as curiosity, intrinsic motivations, task-free reinforcement learning, self-generated goals, and any other
mechanism that might support autonomous learning.
The competition will have a two-phase structure where during a first ‘intrinsic phase’ the system will have a certain time to freely explore and learn in the environment, and then during an `extrinsic phase’ the quality of the autonomously acquired knowledge will be measured with tasks unknown at design
time.
The objective of REAL is to: (a) track the state-of-the-art in robot open-ended autonomous learning; (b)
foster research and the proposal of new solutions to the many problems posed by open-ended learning;(c) favour the development of benchmarks in the field
