RiskAwareBench: Towards Evaluating Physical Risk Awareness for High-level Planning of LLM-based Embodied Agents
<|reference_start|>RiskAwareBench: Towards Evaluating Physical Risk Awareness for High-level Planning of LLM-based Embodied Agents: The integration of large language models (LLMs) into robotics significantly enhances the capabilities of embodied agents in understanding and executing complex natural language instructions. However, the unmitigated deployment of LLM-based embodied systems in real-world environments may pose potential physical risks, such as property damage and personal injury. Existing security benchmarks for LLMs overlook risk awareness for LLM-based embodied agents. To address this gap, we propose RiskAwareBench, an automated framework designed to assess physical risks awareness in LLM-based embodied agents. RiskAwareBench consists of four modules: safety tips generation, risky scene generation, plan generation, and evaluation, enabling comprehensive risk assessment with minimal manual intervention. Utilizing this framework, we compile the PhysicalRisk dataset, encompassing diverse scenarios with associated safety tips, observations, and instructions. Extensive experiments reveal that most LLMs exhibit insufficient physical risk awareness, and baseline risk mitigation strategies yield limited enhancement, which emphasizes the urgency and cruciality of improving risk awareness in LLM-based embodied agents in the future.<|reference_end|>
@article{zhu2024eairiskbench:,
    title={EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task
  Planning of Foundation Model-based Embodied AI Agents},
    author={Zihao Zhu, Bingzhe Wu, Zhengyou Zhang, Lei Han, Baoyuan Wu},
    journal={arXiv preprint arXiv:2408.04449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04449},
    primaryClass={cs.AI}
}