DexHandDiff introduces a breakthrough framework that enables robotic hands to perform complex manipulation tasks with human-like dexterity and adaptability.

• Solves the ghost state problem where objects appear to move without proper contact
• Implements an interaction-aware diffusion planning approach that generates realistic hand-object interactions
• Demonstrates adaptive manipulation capabilities for sequential hand movements across varied tasks
• Achieves superior performance compared to prior diffusion-based methods

This research advances engineering capabilities for factory automation and manufacturing processes by enabling robots to handle objects with greater precision and adaptability, potentially transforming industrial assembly operations.

DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation