Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Dex-One2Many learns a generalizable manipulation policy from one human video by abstracting it into sequential scene graphs. It uses this graph structure to guide reinforcement learning, allowing the robot to learn how to perform complex tasks like grasping objects in new scenarios. This method enables zero-shot transfer from simulation to the real world.