Stanford Seminar - Generalization through Task Representations with Foundation Models

This talk presents an evolution of task representation approaches for enabling zero-shot generalization in robotic manipulation: starting from natural language task representation using large language models, progressing to 3D value maps for spatial task grounding, then to relational keypoint constraints for handling temporal dependencies, and finally to affordance-based representations for enabling generalization across unseen tasks and objects without task-specific demonstrations.