Generative Neural Retargeting for Human-to-Robot Dexterous Manipulation
Generative Neural Retargeting (GNR) is a method for retargeting complex human demonstrations for five-fingered manipulation. It uses a flow matching model to sample feasible trajectories conditioned on human motion, overcoming scaling issues in traditional sampling methods. GNR achieves superior performance in diversity and difficulty compared to sampling-based approaches like MPC by learning from small seed datasets.