DexH2R: Task-oriented Dexterous Manipulation from Human to Robots

Understand

Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects.

• Recent advancements in learning from human demonstrations and teleoperation have enabled progress for robots in such ability.
• However, these approaches either require complex data collection such as costly human effort for eye-robot contact, or suffer from poor generalization when faced with novel scenarios.
• To solve both challenges, we propose a framework, DexH2R, that combines human hand motion retargeting with a task-oriented residual action policy, improving task performance by bridging the embodiment gap between human and robotic dexterous hands.