Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL - Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL, Part2

This is a continuation of highlights of topics covered in the course. This lecture discusses the challenges and goals of modern robotics, mainly focusing on the need for more flexible and adaptable robots. The content for the course can be found on this page (https://fracturedplane.notion.site/Robot-Learning-IFT6163-Scaling-Learning-for-Real-World-Agents-Apprentissage-robotique-Apprentiss-14a2148572768017864af202952c4b7e) The professor highlights the limitations of current robotic systems, which often operate in highly controlled environments and lack the ability to generalize to new situations. He argues that future robots should be able to handle unforeseen circumstances, learn from experience, and adapt to changing environments. To achieve this, the professor emphasizes the importance of developing algorithms that enable generalization across different tasks and robot morphologies. I introduce the concept of minimizing regret over all possible tasks and morphologies, aiming for a system that can learn effectively and efficiently across a wide range of scenarios. The lecture also touches upon the challenges of defining and measuring generalization, emphasizing the need for a clearer understanding of what it means for a robot to truly generalize its knowledge and skills.