Variable-Friction In-Hand Manipulation for Arbitrary Objects via Diffusion-Based Imitation Learning

Understand

Dexterous in-hand manipulation (IHM) for arbitrary objects is challenging due to the rich and subtle contact process.

• Variable-friction manipulation is an alternative approach to dexterity, previously demonstrating robust and versatile 2D IHM capabilities with only two single-joint fingers.
• However, the hard-coded manipulation methods for variable friction hands are restricted to regular polygon objects and limited target poses, as well as requiring the policy to be tailored for each object.
• This paper proposes an end-to-end learning-based manipulation method to achieve arbitrary object manipulation for any target pose on real hardware, with minimal engineering efforts and data collection.