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We introduce a pipeline that enhances a general-purpose Vision LanguageModel, GPT-4V(ision), by integrating observations of human actions tofacilitate robotic manipulation. This system analyzes videos of humansperforming tasks and creates executable robot programs that incorporateaffordance insights. The computation starts by analyzing the videos with GPT-4Vto convert environmental and action details into text, followed by aGPT-4-empowered task planner. In the following analyses, vision systemsreanalyze the video with the task plan. Object names are grounded using anopen-vocabulary object detector, while focus on the hand-object relation helpsto detect the moment of grasping and releasing. This spatiotemporal groundingallows the vision systems to further gather affordance data (e.g., grasp type,way points, and body postures). Experiments across various scenariosdemonstrate this method's efficacy in achieving real robots' operations fromhuman demonstrations in a zero-shot manner. The prompts of GPT-4V/GPT-4 areavailable at this project page:https://microsoft.github.io/GPT4Vision-Robot-Manipulation-Prompts/

GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration