入门具身离不开3个要素，数据+算法+本体

Core Insights - The article emphasizes the importance of three key elements in embodied intelligence: data, algorithms, and embodiment. Many individuals only understand algorithms, while data collection requires experience and effective strategies [1][2] - The community aims to create a platform for knowledge sharing and collaboration in the field of embodied intelligence, targeting a membership of 10,000 within three years [2][6] Data Collection - Remote operation data collection relies on embodiment and is costly, but preprocessing and postprocessing are simpler, yielding high-quality data suitable for robotic arms [1] - The community provides various data collection strategies and high-cost-performance robotic arm platforms to support research [1][2] Algorithm Development - Common technologies in embodied intelligence include VLN, VLA, Diffusion Policy, and reinforcement learning, which require continuous reading of academic papers to stay updated [1] - The community offers a comprehensive set of learning paths and resources for newcomers and advanced researchers alike [9][12] Hardware and Resources - Well-funded laboratories can purchase high-cost embodiment systems, while those with limited budgets may rely on 3D printing or cost-effective hardware platforms [1] - The community has compiled a list of over 40 open-source projects and nearly 60 datasets related to embodied intelligence, along with mainstream simulation platforms [9][26][28] Community Engagement - The community has established connections with various companies in the field, creating a bridge for academic collaboration, product development, and recruitment [2][6] - Members can access job postings, industry insights, and a supportive environment for learning and networking [5][12] Educational Content - The community provides a wealth of educational materials, including summaries of research papers, books, and learning routes across various topics in embodied intelligence [10][18][20] - Regular discussions and Q&A sessions are held to address common challenges in the field, such as data collection platforms and robot learning techniques [11][12]