Efficient Motor Skills Learning in Robotics🔗
Abstract🔗
As a fundamental cornerstone in the development of intelligent robotic systems, the research community on robot learning has addressed autonomous motor skill learning and control in complex task scenarios. Imitation learning provides an efficient way to learn new skills through human guidance, which can reduce the time and cost to program the robot. Robot learning architectures can provide a comprehensive framework for learning, recognition, and reproduction of whole-body motions. The inference mechanism can be applied not only to learn the robot's free body motion but also to learn physical interaction tasks, including human-robot interaction. In this talk, I will introduce robot learning algorithms including learning from human demonstrations, incremental learning, and the extension of learning from simple movement primitives to complex tasks, such as context-aware manipulation tasks, and human-robot collaboration.