Leveraging Pretrained Latent Representations for Efficient Few-Shot Imitation Learning on a Dexterous Robotic Hand

This work proposes a method to leverage pretrained latent representations of human hand motion to improve the robustness and sample efficiency of behavior cloning for dexterous manipulation tasks on a robotic hand, eliminating the need for costly teleoperation-based data collection.