RI Seminar: Nikolai Matni : What Makes Learning to Control Easy or Hard?

RI Seminar: Nikolai Matni : What Makes Learning to Control Easy or Hard?
Share:

Download MP3


Similar Tracks

RI Seminar: Dieter Fox: Where's RobotGPT? CMU Robotics Institute
RI Seminar: Guanya Shi : Building Generalist Robots with Agility... CMU Robotics Institute
IST Research Talks: Syed Billah ISTatPENNSTATE
Control Opportunities in Electrified Homes Control Seminar
Jan Janssen: How to use machine learning in the discovery and design of materials for the future? MICDE University of Michigan
David Mount: Differentiable Approximations for Distance Queries Computational Geometry
RI Seminar: Yong-Lae Park : Bio-Inspired Soft Robotics: New Ways of Sensing and Actuation CMU Robotics Institute
Iris Recognition by Prof. John Daugman SAIConference
The safe gradient flow: a system-theoretic approach to anytime constrained optimization. Control Seminar
RI Seminar: Saurabh Gupta : Robot Learning by Understanding Egocentric Videos CMU Robotics Institute
12/05 Dr. Bhamla Colloquium Department of Physics - Arizona State University
RI Seminar: Robert Katzschmann : Robots Based on Musculoskeletal Designs CMU Robotics Institute
Data Analytics for Beginners | Data Analytics Training | Data Analytics Course | Intellipaat Intellipaat
TRR Seminar Series: "Scattering of surface waves by ocean currents", by Han Wang TRR 181 Energy transfers in Atmosphere and Ocean
SESSION 1 | Multi-Agent Reinforcement Learning: Foundations and Modern Approaches | IIIA-CSIC Course IIIA-CSIC
RI Seminar : Jeffrey Ichnowski : Learning for Dynamic Robot Manipulation of Deformable... CMU Robotics Institute
RI Seminar: Nils Napp : Abstraction Barriers for Embodied Algorithms CMU Robotics Institute
Reinforcement Learning: Machine Learning Meets Control Theory Steve Brunton
Weijie Su: How Statistics Can Advance Large Language Models: Fairness Alignment and Watermarking ASA Statistical Learning and Data Science
AIR Distinguished Speaker: Dr. Sung Ju Hwang: Efficient and Long-Context GenAI Models Hariri Institute for Computing, Boston University