Deep Imitation Learning for Efficient Path-Following of Hyper-Redundant Robots

Abstract

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 feedback loops; conversely, geometric heuristics offer speed but lack kinematic fidelity. To resolve this efficiency-accuracy trade-off, we present an imitation learning framework tailored for path-following tasks. First, to address the instability of expert data generation caused by non-differentiable minimax objectives, we propose a refined Soft-Maximum formulation that produces smooth, kinetically consistent demonstrations. Second, we mitigate the covariate shift inherent in Behavior Cloning (BC) through a two-stage noise-injection curriculum, enabling the agent to learn robust recovery policies entirely offline without requiring an interactive expert. Finally, we design a structured policy network that effectively fuses high-dimensional path descriptors with low-dimensional proprioceptive states. Extensive simulations demonstrate that our approach achieves optimization-level accuracy with inference speeds comparable to geometric heuristics, validating its efficacy for high-precision inspection tasks.