机械手真正「活」了，银河通用&清华推出DexNDM，用神经动力学重塑灵巧操作

Core Insights - The article discusses the development of DexNDM, a method aimed at solving the sim-to-real challenge in dexterous robotic manipulation, particularly in achieving stable in-hand rotation of various objects [2][5][24]. Group 1: Background and Challenges - High dexterity in remote operation of complex tools, such as using a screwdriver or hammer, has been a long-standing challenge in robotics [4]. - Traditional direct mapping remote operation methods are limited to simple tasks and cannot handle complex manipulations requiring fine motor skills [4]. - A semi-autonomous remote operation paradigm is proposed, which breaks down complex tasks into stable atomic skills that robots can execute autonomously [4]. Group 2: DexNDM Methodology - DexNDM is designed to learn general and stable atomic skills for in-hand rotation, covering a wide range of scenarios including challenging elongated and small objects [5][19]. - The method utilizes a joint-wise neural dynamics model to bridge the gap between simulation and real-world dynamics, enhancing data efficiency and generalization across different hand-object interactions [19][20]. Group 3: Achievements and Capabilities - DexNDM achieves unprecedented capabilities in continuous rotation of objects under challenging wrist postures, demonstrating superior performance compared to previous methods [9][13]. - The system allows operators to guide dexterous hands in complex tasks such as tightening screws and assembling furniture, showcasing its robustness and adaptability [7][15]. - The method's flexibility enables stable execution of tasks regardless of the wrist orientation or rotation axis required [14][15]. Group 4: Data Collection and Training - An automated data collection system, termed "Chaos Box," is developed to gather diverse real-world interaction data with minimal human intervention [21]. - A residual policy network is trained to compensate for the dynamics gap between simulation and reality, enhancing the system's performance in real-world applications [23]. Group 5: Conclusion and Future Outlook - DexNDM represents a significant advancement in addressing the sim-to-real challenge in robotics, achieving dexterous manipulation skills previously deemed impossible [24]. - The authors believe this is just the beginning, with the potential for dexterous hands to play a crucial role in the future of humanoid robotics [25].