Building Adaptable Generalist Robots
Offered By: Montreal Robotics via YouTube
Course Description
Overview
Explore the cutting-edge advancements in deep robot learning and the challenges of building adaptable generalist robots in this 48-minute talk by Mengdi Xu from Montreal Robotics. Delve into innovative approaches for improving robot generalization, including in-context learning from demonstrations, unsupervised continual reinforcement learning, and leveraging large foundation models for embodied agents. Discover how these techniques enhance data efficiency, parameter efficiency, and robustness, enabling robots to acquire new motor skills and solve complex physical puzzles with creative tool use. Gain insights into the future of robotics as Xu, a Ph.D. student at Carnegie Mellon University, shares her research on building learning-based robots capable of reliably interacting with the unstructured real world and adapting to unseen tasks beyond their training sets.
Syllabus
Mengdi Xu: Building Adaptable Generalist Robots
Taught by
Montreal Robotics
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera 機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera Leading Ambitious Teaching and Learning
Microsoft via edX