Self-Supervised and Continual Representation Learning in Unstructured Environments
Speaker: Peyman Moghadam – Brisbane, QLD, AustraliaTopic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Abstract
In today's robotic applications, learning-based methods play a key role in tasks like (re)-localization, perception, navigation, and manipulation. However, these methods often face challenges when generalizing to new environments due to domain gaps and the scarcity of large-scale, labelled data. This lecture will explore alternative paradigms in which robots incrementally learn representations from sequences of environments while addressing the issue of catastrophic forgetting. It will delve into strategies for adapting pre-trained large models in the face of significant domain shifts, using self-supervised methods that do not rely on ground-truth supervision.
About this Lecture
Number of Slides: 30 - 40Duration: 45 - 60 minutes
Languages Available: English
Last Updated:
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