Core Viewpoint - The article discusses the challenges and advancements in dexterous manipulation in robotics, highlighting the need for high-quality, multi-modal data to improve robotic grasping capabilities and the introduction of the DexCanvas dataset as a solution [1][15]. Group 1: Challenges in Dexterous Manipulation - Dexterous manipulation remains a significant challenge due to the need for precise control, high-dimensional motion planning, and real-time adaptation to dynamic environments [2][11]. - Existing hardware for dexterous manipulation is categorized into two types: two-finger grippers and multi-finger humanoid hands, with the latter being more suitable for complex tasks due to their higher degrees of freedom [2][3]. - Current learning methods for dexterous manipulation include imitation learning and reinforcement learning, each with its own advantages and limitations regarding data requirements and training complexity [4][9]. Group 2: Data Collection and Quality Issues - Data collection for dexterous manipulation is expensive and often lacks tactile and force information, with existing datasets being insufficient for large-scale pre-training [9][10]. - The article emphasizes the trade-off in data collection, where achieving scale, realism, and tactile feedback simultaneously is challenging [6][7]. - The DexCanvas dataset addresses the lack of force and tactile information in existing datasets, providing a comprehensive solution for high-quality data collection [17][21]. Group 3: DexCanvas Dataset Introduction - DexCanvas is a large-scale dataset launched by Lingqiao Intelligent Technology, designed to bridge the gap between cognitive and physical intelligence in robotics [15][16]. - The dataset includes complete multi-finger force/contact annotations optimized for systems with over 20 degrees of freedom, significantly enhancing data quality [17][21]. - DexCanvas offers a structured framework for data collection based on 22 types of human hand operation modes, integrating over 1,000 hours of real human demonstration data and 100,000 hours of physically simulated data [21][22]. Group 4: Data Generation and Enhancement - The dataset generation process involves capturing human demonstrations with high precision and using physical simulation to recover missing force control data [25][27]. - DexCanvas expands the dataset by altering object properties and initial conditions, resulting in a significant increase in data volume while maintaining force control information [28][29]. - Unlike pure simulation, DexCanvas is based on real human demonstrations, allowing for better generalization across different robotic platforms and tasks [30]. Group 5: Industry Impact and Future Prospects - The introduction of DexCanvas is expected to accelerate advancements in the field of robotics by providing essential data for physical interaction, which has been lacking in existing datasets [32]. - The article expresses anticipation for the open-sourcing of the dataset to further enhance research and development in related areas [32].

今日，国产灵巧手赛道头部企业灵巧智能重磅出击，向外界分享其在机器人灵巧操作领域的最新研究成 果， 正式发布灵巧智能DexCanvas数据集 。该数据集规模达20TB，包含1000小时真人操作记录，是涵盖多模态 人手操作数据的重磅资源，将为机器人灵巧操作领域注入强劲动力。 ▍成本、规模、真实性难以兼得，具身智能数据采集困境待解 当前 AI在物理世界的应用中，虽已实现理解人类语言、识别物体与场景、规划任务步骤等能力，但 在物理世 界中的 "最后一公里"，即让机器人像人类一样灵活地抓握、理解语言、识别物体和场景、感知并调节力度、 适应不同物体等方面，仍是一个待突破的难题。 这一瓶颈很大程度上源于当前大规模、高质量、多模态交互 数据集的缺乏。 一般来说，机器人在实际场景中的操作表现往往受到感知不确定性、动力学复杂性和环境变化敏感性的制约， 也因此 数据集的规模与质量直接决定了模型在真实环境中的表现。 从技术实现路径来看，具身智能操作的数 据采集方式目前主要为遥操作、 视频学习和仿真合成。 遥操作通过专业设备记录人类专家的动作和力控信息，能获得高质量、高精度的真实数据，尤其适合精密力控 任务，但存在设备昂贵、效率低以及 ...