Workshop on Self-Supervised Robot Learning

Self-supervised learning is an exciting research direction that aims to learn representations from the data itself without explicit and potentially even manual supervision. One of the major benefits of self-supervised learning is the ability to scale to large amounts of unlabelled data in a lifelong learning manner and to improve performance by reducing the effect of dataset bias. Recent development in self-supervised learning has resulted in achieving comparable or better performance than fully-supervised models. However, many of these methods are developed in domain-specific communities such as robotics, computer vision or reinforcement learning. The aim of this workshop is to bring together researchers from different communities to discuss opportunities, challenges and explore new directions.