Reachable Manifold and Inverse Mapping Training for Robots

This paper presents a novel approach for training robotic systems using reachable manifold representations and inverse mapping techniques. We develop methods that enable robots to learn complex manipulation tasks by understanding the geometric constraints of their workspace and efficiently mapping between task space and configuration space. Our approach demonstrates improved performance in dexterous manipulation and planning tasks.

Conference paper

IEEE International Conference on Robotics and Automation