Agile Collision Avoidance for Deformable-Tethered Multi-Robot Systems Via Zone-Aware Hierarchical Learning and VLM-Guided Control

Abstract

Navigating Linked Multi-Component Robotic Systems (L-MCRS)---robot pairs tethered by passive flexible hoses---through dynamic pedestrian environments is fundamentally harder than rigid multi-robot coordination, as the uncontrollable hose creates a variable-geometry collision footprint spanning 118 pairwise combinations. We propose H-SEPID, unifying zone-aware Hierarchical Reinforcement Learning grounded in Kinematic Flow Theory with VLM-guided cascaded optimization. A phase-aware dual attention value network performs C0-continuous topological policy switching, while a Vision-Language Model infers strategic intent and quantifies action-space constraints governing hose geometry. A seven-category safety shield with ORCA fallback and a threading reward band produce emergent gap-threading maneuvers. H-SEPID achieves 94 success and 4 collision rate in an 8-robot, 5-pedestrian, 4-hose scenario, outperforming five baselines, and is validated on real e-puck2 robots across 12 configurations.