DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
This addresses the challenge of adaptive dexterous manipulation with contact-rich interactions for robotics, representing a strong specific gain rather than a foundational advance.
The paper tackled the problem of unrealistic ghost states and lack of adaptability in diffusion-based planning for dexterous manipulation by introducing DexHandDiff, an interaction-aware framework that achieved over twice the average success rate (59.2% vs. 29.5%) compared to existing methods and 70.7% on goal-adaptive tasks.
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks, they often produce unrealistic ghost states (e.g., the object automatically moves without hand contact) or lack adaptability when handling complex sequential interactions. In this work, we introduce DexHandDiff, an interaction-aware diffusion planning framework for adaptive dexterous manipulation. DexHandDiff models joint state-action dynamics through a dual-phase diffusion process which consists of pre-interaction contact alignment and post-contact goal-directed control, enabling goal-adaptive generalizable dexterous manipulation. Additionally, we incorporate dynamics model-based dual guidance and leverage large language models for automated guidance function generation, enhancing generalizability for physical interactions and facilitating diverse goal adaptation through language cues. Experiments on physical interaction tasks such as door opening, pen and block re-orientation, object relocation, and hammer striking demonstrate DexHandDiff's effectiveness on goals outside training distributions, achieving over twice the average success rate (59.2% vs. 29.5%) compared to existing methods. Our framework achieves an average of 70.7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.