用视觉语言模型提升人机协作装配的感知与规划鲁棒性

Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and human interventions. To address these challenges, we introduce a design-grounded human-aware planning framework for human-robot collaborative structured assembly. The framework comprises two coupled modules. Module I, Perception-to-Symbolic State (PSS), employs vision-language models (VLMs) based agents to align RGB-D observations with design specifications and domain knowledge, synthesizing verifiable symbolic assembly states. It outputs validated installed and uninstalled component sets for online state tracking. Module II, Human-Aware Planning and Replanning (HPR), performs task-level multi-robot assignment and updates the plan only when the observed state deviates from the expected execution outcome. It applies a minimal-change replanning rule to selectively revise task assignments and preserve plan stability even under human interventions. We validate the framework on a 27-component timber-frame assembly. The PSS module achieves 97% state synthesis accuracy, and the HPR module maintains feasible task progression across diverse HRC scenarios. Results indicate that integrating VLM-based perception with knowledge-driven planning improves robustness of state estimation and task planning under dynamic conditions.