Towards Democratizing Joint-Embedding Self-Supervised Learning
Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data. The development of JE-SSL methods was driven primarily by the search for ever increasing downstream classification accuracies, using huge computational resources, and typically built upon insights and intuitions inherited from a close parent JE-SSL method. In this work, we debunk several such ill-formed a priori ideas in the hope to unleash the full potential of JE-SSL free of unnecessary limitations.
Provides actionable insights to improve Joint Embedding Self-Supervised Learning (JE-SSL) by debunking misconceptions and introducing an optimized PyTorch library for SSL.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research. Alexa Arena provides a variety of multi-room layouts and interactable objects, for the creation of human-robot interaction (HRI) missions. With user-friendly graphics and control mechanisms, Alexa Arena supports the development of gamified robotic tasks readily accessible to general human users, thus opening a new venue for high-efficiency HRI data collection and EAI system evaluation.
Provides a platform for developing embodied agents for robotic task completion challenges, with a focus on advancing research in Human Robot Interaction (HRI).