Planning Neural Dynamics with Lie Group Embedding through Supervised Projective Manifold Learning

This research proposes Lie group embedded dynamical neural networks (LieEDNN) and associated learning algorithms for achieving learnable and stable dynamics on underlying manifolds. It leverages Lie groups' powerful representation capabilities to address challenges in neural network interactions and nonlinear representation spaces, with applications in robotics, graphics, and control.