Structuring Manipulation Tasks for More Efficient Learning

Explore a robotics colloquium lecture on structuring manipulation tasks for more efficient learning. Delve into the future of robotics and the creation of versatile robots capable of operating in unstructured environments. Learn about modularity in breaking down complex manipulation tasks, model-based reasoning for skill adaptation, and the use of interactions and multimodal sensing for learning manipulation-oriented representations of materials. Gain insights from Dr. Oliver Kroemer, an assistant professor at Carnegie Mellon University's Robotics Institute, as he shares recent work from his Intelligent Autonomous Manipulation Lab. Discover approaches to developing algorithms and representations that enable robots to learn robust and versatile manipulation skills for a wide range of objects and task scenarios.