Open-ended learning, also named "life-long learning", "autonomous curriculum learning", and "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 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 explore and learn in the environment freely, 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:
Participation in the competition, which leads to addressing key problems relevant to the ICDL community, is free, and everyone is welcome to participate!
|Round 1 starts||
|Round 1 ends||Thursday, 15th October 2020|
|Round 2 starts||Sunday, 1st November 2020|
|Round 2 ends||Sunday, 31st January 2021|
|Final evaluations||Monday, 15th February 2021|
You can find more background and the rules of the competition, download the software to develop your models in your computer, and take part in the competition here:Competition page