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.

翻译：结构化装配中的人机协作需要在噪声感知和人为干预下实现可靠的状态估计与自适应任务规划。为应对这些挑战，本文提出一种基于设计信息的人类感知规划框架，用于人机协作结构化装配。该框架包含两个耦合模块：模块I（感知到符号状态）采用基于视觉-语言模型的智能体，将RGB-D观测数据与设计规范及领域知识对齐，合成可验证的符号化装配状态，并输出经验证的已安装/未安装组件集合以支持在线状态追踪；模块II（人类感知规划与重规划）执行任务层级的多机器人分配，仅在观测状态偏离预期执行结果时更新规划方案，通过最小变更重规划规则选择性地修正任务分配，确保即使在人为干预下仍能保持规划稳定性。我们在包含27个构件的木框架装配任务中验证了该框架：PSS模块实现了97%的状态合成准确率，HPR模块在多样化人机协作场景中始终保持可行的任务推进。结果表明，将基于VLM的感知与知识驱动规划相结合，能有效提升动态条件下状态估计与任务规划的鲁棒性。