Safe-Mobile-Manipulation

Introspective Planning: Aligning Robots’ Uncertainty with Inherent Task Ambiguity 🌐 Project Page | 📄 Paper | GitHub Overview Safe Mobile Manipulation dataset was designed to evaluate Large Language Models' (LLMs) capability to reason effectively about both uncertainty and safety in mobile manipulation tasks. The dataset comprises 500 total scenarios: 100 scenarios in the test set, 200 scenarios for knowledge base construction, and 200 scenarios for conformal calibration.… See the full description on the dataset page: https://huggingface.co/datasets/kaiquliang/Safe-Mobile-Manipulation.