A new benchmark called DESPITE has been developed to systematically evaluate the safety risks associated with using large language models for embodied planning in robotics. Research indicates that even models with high planning accuracy can exhibit significant safety failures, with safety awareness not scaling proportionally with model size. The findings highlight that improving safety awareness is a critical challenge for deploying LLM-based planners in real-world robotic systems. AI
影响 Highlights critical safety challenges for LLM-based robotic planners, emphasizing the need for improved danger avoidance over mere planning ability.
排序理由 The cluster contains two arXiv papers discussing safety risks in AI, specifically concerning LLMs in embodied planning and a broader survey of safety in embodied AI.
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
