细节铺满！行业首个开源的灵巧操作真机数据集，解决机器人“看得见摸不准”的问题

Core Viewpoint - The article emphasizes the significance of the newly open-sourced high-quality tactile operation dataset for dexterous robotic hands, which addresses the industry's urgent need for accurate physical interaction data and is expected to drive advancements in humanoid robotics in 2026 [6][7][42]. Group 1: Challenges in Dexterous Manipulation - Dexterous manipulation is challenging due to three main factors: the lack of mature hardware products, difficulties in model training that rely solely on visual data, and a scarcity of high-quality tactile data [6][9]. - The current limitation of dexterous manipulation is primarily due to the inability to effectively perceive physical properties like force and material through visual data alone, leading to the issue of "seeing but not touching" [9][24]. Group 2: Open-Sourced Dataset Details - The dataset consists of 800 high-quality tactile operation samples, which provide a continuous multi-modal learning resource that connects "visual-force-touch-action" [10][11]. - The dataset includes real-world scenarios such as fruit grabbing, package sorting, and material loading, ensuring a realistic representation of complex operational environments [9][12]. - It features multi-modal data with added tactile and six-dimensional force data, enhancing the robot's ability to perceive physical attributes of objects [9][11]. Group 3: Technical Advancements - The dataset introduces five key enhancements: arrayed tactile data, higher-dimensional force control data, 3D spatial information, synchronized perception of visual and tactile data, and a broad range of real-world scenarios to prevent overfitting [15][16]. - The tactile data is collected using a 6×12×5 sensor array, allowing the robotic hand to accurately sense material properties and contact states, while the six-dimensional force data provides high precision [15][20]. Group 4: Impact on Robotics - The open-sourced dataset is expected to improve the success rate of robotic operations by enabling real-time perception and adjustment of grasping techniques based on object shape and force [25][26]. - The integration of tactile and visual data allows robots to break through the limitations of pure visual perception, enhancing operational stability in complex environments [26][27]. - The dataset's broad coverage across various fields, including household, logistics, and consumer goods, will facilitate the adaptation of robots to different materials and shapes [27][31]. Group 5: Future Prospects - The open-sourcing of this dataset is anticipated to catalyze the development of the entire embodied intelligence industry chain, fostering innovation and application in robotics [40][41]. - The establishment of the Leju OpenLET community aims to create a collaborative platform for developers and researchers, accelerating the development and industrial application of embodied intelligence technologies [43].