发表机构
Shanghai Jiao Tong University; Eastern Institute of Technology, Ningbo; University of Waterloo; Stony Brook University; University of Science and Technology of China; Chengdu Institute of Computer Applications, Chinese Academy of Sciences; Ningbo Institute of Digital Twin（上海交通大学; 宁波东方理工大学; 滑铁卢大学; 纽约州立大学石溪分校; 中国科学技术大学; 中国科学院成都计算机应用研究所; 宁波数字孪生研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有基准未联合评估卫生风险识别与安全规划的问题，提出HygieneRoboBench基准（624实例）和Hygiene-NSP方法，结合LLM、接触历史重建与CP-SAT，实现94.4%安全解决率，优于基线。
AI 中文摘要
接触受污染物体可能通过家用机器人的夹爪、工具和共享表面传播危害，而新的接触可能使现有计划变得不安全。现有基准测试并未联合评估规划器如何从接触历史中识别卫生风险，并在新的接触事件发生后规划安全的后续行动。规划器必须在时间和资源限制内，并尊重用户优先级的情况下做到这一点。我们引入了HygieneRoboBench，包含134个任务族中的624个实例，用于评估在给定执行历史下家庭任务的安全解决。任务通过两个夹爪和共享物体捕获污染、处理成本和用户优先级。我们将受控历史、配置文件和事件比较与独立计划评估相结合。这些评估安全解决、用户优先级下的成本效率以及对接触事件的响应。对基于LLM和符号规划器的评估表明，安全完成任务并不能保证在用户优先级下实现最低执行成本。为解决此问题，我们引入了Hygiene-NSP。它结合了基于LLM的接地、接触历史重建和CP-SAT，在用户优先级下联合规划卫生处理和任务执行。Hygiene-NSP实现了94.4%和90.4%的安全解决率和最优安全解决率。这两个比率均高于在完整数据集上评估的基线规划器。项目页面：此https URL。
英文摘要
Contact with contaminated objects can spread hazards through a household robot's grippers, tools, and shared surfaces, while new contacts can make an existing plan unsafe. Existing benchmarks do not jointly assess how planners identify hygiene risks from contact history and plan safe continuations after new contact events. Planners must do so within time and resource limits while respecting user priorities. We introduce HygieneRoboBench, with 624 instances across 134 task families, to evaluate safe resolution of household tasks from a given execution history. Tasks capture contamination through two grippers and shared objects, treatment costs, and user priorities. We combine controlled history, profile, and event comparisons with independent plan evaluation. These assess safe resolution, cost efficiency under user priorities, and responses to contact events. Evaluation of LLM-based and symbolic planners shows that safely completing a task does not guarantee the lowest execution costs under the user's priorities. To address this problem, we introduce Hygiene-NSP. It combines LLM-based grounding, contact-history reconstruction, and CP-SAT to jointly plan hygiene treatment and task execution under user priorities. Hygiene-NSP achieves safe resolution and optimal safe resolution rates of 94.4% and 90.4%, respectively. Both rates are higher than those of the evaluated baseline planners on the full dataset. Project page: https://euron-zc.github.io/HygieneRoboBench/.
