Deep Imitation Learning for Efficient Path-Following of Hyper-Redundant Robots
Hyper-redundant robots are essential for navigation in highly constrained environments, yet their high-dimensional kinematics impose a severe computational burden on real-time motion planning. While optimization-based methods ensure tracking precision, their high computational latency makes them unsuitable for online f...