Uncertainty Adaptive Shared Control with Multimodal Intent Fusion for Human-Guided Quadruped Locomotion on Uneven Terrain

Abstract

Human-guided quadruped locomotion requires re-liable intent inference and safe authority allocation under un-certain interaction and uneven terrain. This study proposes an uncertainty-adaptive shared-control framework with multimodal intent fusion for a Unitree Go2 quadruped. Interaction force, relative human–robot motion, operator-motion cues, and terrain context are fused through a temporal model to estimate desired motion and maneuver mode. Intent confidence is evaluated using posterior entropy and prediction variance, while locomotion risk is computed from traversability, slope, obstacle proximity, interaction load, and stability margin. The human authority factor is then adjusted online to blend the inferred human command with a robot-safe command. The shared command is realized through gait scheduling, terrain-aware foothold optimization, whole-body control, and interaction-compliant base regulation. Experiments across five guidance scenarios show that the proposed method improves intent accuracy, interaction load, slip resistance, stability margin, and task success compared with force-only guidance, fixed-authority shared control, and ablated variants.