机器人“会用手”了！银河通用首破手掌任意朝向旋转难题，拧螺丝、砸钉子样样精通

Core Insights - The article discusses the breakthrough of the DexNDM model developed by Galaxy Universal, which enables dexterous hands to perform complex tasks such as in-hand rotation and tool usage, bridging the gap between simulation and real-world applications [2][4][55]. Group 1: DexNDM Model Capabilities - DexNDM allows for stable in-hand rotation of various objects, regardless of their size or shape, achieving cross-object and cross-pose manipulation [5][6]. - The model can operate under challenging wrist postures, enabling continuous rotation of long objects and stable manipulation of small items [6][17]. - It enhances the robot's ability to perform complex tasks like screw tightening and furniture assembly, marking a significant leap from simple grasping to dexterous manipulation [21][64]. Group 2: Technical Innovations - DexNDM employs a joint-wise neural dynamics model, allowing each joint to independently predict its next state, improving data efficiency and generalization across different tasks [8][10]. - The model utilizes an automated data collection strategy to generate rich contact data without manual intervention, enhancing learning efficiency [11][14]. - A residual policy network is trained to bridge the gap between simulation and reality, facilitating the transfer of learned strategies to real-world scenarios [15]. Group 3: Importance of Dexterous Manipulation - Dexterous manipulation is crucial for robots to transition from basic capabilities to productive tasks, as it encompasses both motion and operational abilities [24][28]. - The ability to perform in-hand rotation and tool usage is seen as a pinnacle of dexterous manipulation, representing a significant challenge in robotics research [37][38]. - The advancements in dexterous manipulation are expected to lead to robots that can perform a wide range of tasks, moving beyond simple demonstrations to actual productive capabilities [58][65].