EmbodiChain开源，用100%生成式数据自动训练具身智能模型

Core Insights - The article discusses the limitations of traditional data collection methods in robotics and emphasizes the need for innovative approaches to generate high-quality interactive data that adheres to physical laws [2][3] - It introduces the concept of "Efficiency Law," which posits that the performance of models is directly related to the rate of data generation, highlighting the necessity for a shift from data scarcity to data abundance in embodied intelligence [5][8] - The launch of EmbodiChain is presented as a foundational step towards creating a generative simulation world model (GS-World), which aims to automate data generation and enhance the learning paradigm for embodied intelligence [13][19] Data Collection Paradigms - The scarcity and high cost of 3D calibrated data for robotics have made data collection paradigms a focal point in industry research [2] - The industry is moving towards more cost-effective and convenient data collection methods, transitioning from expensive remote operation devices to innovative solutions that require minimal human intervention [2] - The article highlights the importance of digitizing human skills to bridge the gap between human experience and robotic actions [2] Challenges in Embodied Intelligence - Current physical data collection methods cannot match the scale required for training large language models (LLMs), which presents a significant barrier to advancing embodied intelligence [3] - The article identifies the slow data generation rate as a bottleneck, where even large model parameters cannot compensate if the model is not adequately fed with data [8] Efficiency Law and Data Generation - The concept of "Efficiency Law" suggests that the relationship between model performance and data generation rate is crucial for the evolution of intelligence [17] - The article argues that in the era of embodied intelligence, data must be generated incrementally, requiring the ability to create data rather than merely cleaning existing datasets [7][14] EmbodiChain and GS-World - EmbodiChain is introduced as a data and model platform that aims to revolutionize the learning paradigm for embodied intelligence by enabling high-speed, automated data generation [13][15] - The article outlines three core scientific challenges that EmbodiChain seeks to address: automating data production, bridging the "Sim2Real Gap," and overcoming the "IO wall" in data generation [16] Comparison of Approaches - The article contrasts the GS-World approach, which focuses on generating physically accurate models, with the video generation route that has shown weaknesses in maintaining long-term temporal consistency [24][25] - It emphasizes the importance of a 3D, interactive, and physically rigorous world model for effective training of robots [30] Results and Future Vision - The results from training the Sim2Real-VLA model using only generated data demonstrate superior performance compared to traditional methods, showcasing the potential of the proposed approach [28][38] - The vision for GS-World extends beyond current capabilities, aiming to create a self-sustaining infrastructure for embodied intelligence research that alleviates the constraints of data scarcity [34][35]