Safety专业
NEUPRO用神经符号规则让机器人安全推理可解释
预印本论文提出NEUPRO，一个神经符号框架，将安全规范表示为一阶逻辑规则，并通过可微分推理器从图像中学习安全谓词。在真实机器人数据集REASON上，NEUPRO在七个任务的安全分类准确率达到0.92±0.02，显著优于VLM基线，并能解释安全违规原因。该工作为机器人安全提供了可解释且可迁移的表示，但尚未经同行评审。
研究
论文
机器人与具身智能领域的最新论文，以及每篇对产业的意义。
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Safety专业
Belief-Aware Multi-Agent Path Finding under Map Uncertainty
Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local disturbances.
Safety专业
ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving
Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual planning benchmark that uses VLM-assisted screening, localized multi-view editing, and quality auditing to insert hazards into real nuScenes scenes while preserving their context.
论文来自 arXiv 机器人领域。尚未生成摘要的论文，显示摘要开头部分。