Uploaded April 2024 | Updated September 2026, 9 hours ago
Microsoft Future Leaders in Robotics and AI Seminar Series: The Foundation Model Path to Open-World Robots
Online Seminar
Dhruv Shah
PhD Candidate
University of California Berkeley
Robot learning methods typically rely either on learning from large-scale simulation modeling and transferring to real-world settings or by collecting real-world interaction data on the target robot. While this paradigm has been successful for solving simple tasks in structured environments, it may fall short for tasks that are hard to simulate accurately (e.g. in off-road racing) and where data collection may be expensive (e.g. micro UAVs)
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
Microsoft Future Leaders in Robotics and AI Seminar Series: The Foundation Model Path to Open-World Robots
Online Seminar
Dhruv Shah
PhD Candidate
University of California Berkeley
Robot learning methods typically rely either on learning from large-scale simulation modeling and transferring to real-world settings or by collecting real-world interaction data on the target robot. While this paradigm has been successful for solving simple tasks in structured environments, it may fall short for tasks that are hard to simulate accurately (e.g. in off-road racing) and where data collection may be expensive (e.g. micro UAVs)
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
