REAL2021 has started! Join the competition and submit your solutions on: https://eval.ai/web/challenges/challenge-page/1134/overview
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:
- track the state-of-the-art in robot open-ended autonomous learning;
- foster research and the proposal of new solutions to the many problems posed by open-ended learning;
- favour the development of benchmarks in the field.
Participation in the competition, which leads to addressing key problems relevant to the ICDL community, is free, and everyone is welcome to participate!
- Prizes: The members of the top 3 teams will receive free registrations for ICDL 2021 and invited to co-author a shared paper.
|Competition starts||Monday, 23th August 2021|
|Competition ends||Wednesday, 15th December 2021|
|Final evaluations||Thursday, 23th December 2021|