20TB、1000小时真人操作记录、超100万种操作状态！灵巧智能发布DexCanvas数据集，炸穿灵巧操作研发门槛！

Core Viewpoint - The article highlights the launch of the DexCanvas dataset by Lingqiao Intelligent, which aims to address the challenges in robot dexterous manipulation by providing a large-scale, high-quality multimodal dataset for training robotic models [1][10]. Data Collection Challenges - Current AI applications in the physical world face difficulties in achieving dexterous manipulation akin to human capabilities due to a lack of large-scale, high-quality multimodal interaction datasets [2]. - The performance of robots in real-world scenarios is constrained by perception uncertainty, dynamic complexity, and sensitivity to environmental changes, making the quality and scale of datasets crucial for model performance [2]. Data Collection Methods - The three main data collection methods are: - Remote Operation: High precision and authenticity but expensive and inefficient with mapping challenges [3][4]. - Video Learning: Low cost and easy to scale but lacks detailed action and perception information, leading to execution inaccuracies [3][4]. - Simulation Synthesis: Efficient and diverse but suffers from physical discrepancies when transitioning from simulation to reality [3][4]. Limitations of Current Methods - Each data collection method has significant limitations, necessitating a solution that efficiently and cost-effectively gathers physically realistic data to support generalized strategy learning for robotic dexterous manipulation [5]. DexCanvas Dataset Innovations - The DexCanvas dataset introduces a new data acquisition mechanism centered on human-object interaction, capturing multimodal information such as motion trajectories, object attributes, and contact force data [6][8]. - It achieves millimeter-level geometric accuracy and reliable contact information, addressing errors from traditional motion capture methods [8]. - A semantic rule extraction method is proposed to manage the complexity of high-dimensional state spaces, abstracting 33 operation prototypes and 6 key semantic parameters for structured task representation [8]. Impact of DexCanvas Dataset - The DexCanvas dataset provides a substantial, reliable resource for both academia and industry, significantly lowering the research barrier in the field and accelerating the application of embodied intelligence models in real-world scenarios [10]. - It establishes a high-quality data benchmark for training dexterous manipulation models and explores a development path that integrates physical rules with semantic abstraction, contributing to the advancement of robotic operational capabilities [10].