都在研究具身，但相当一部分同学卡在了这些地方.......

Core Insights - The article discusses the challenges faced by individuals in the field of embodied intelligence, particularly in areas such as computational power, data collection, model optimization, and practical project implementation [1][2][6] - It emphasizes the importance of quality data collection and suggests starting with basic teleoperation to mitigate noise in data, which can hinder model training [1] - The community has established a platform for sharing knowledge, resources, and job opportunities in the field of embodied intelligence, aiming to cultivate talent and facilitate industry connections [2][12][16] Data Collection - Recommendations for data collection include focusing on the quality of data and starting with basic teleoperation techniques [1] - The article highlights the potential of using real2sim2real methods to address insufficient data issues [1] Model Optimization - For those using robotic arms, the article suggests exploring RL+VLA approaches, while cautioning against complex models for humanoid robots due to the difficulty in achieving effective results [1] Community and Resources - The community has organized various resources, including technical routes for beginners, industry-related project solutions, and job referral mechanisms with multiple companies in the field [2][10][12] - A comprehensive list of over 40 open-source projects and 60 datasets related to embodied intelligence has been compiled to assist members in their research and development efforts [13][28][34] Learning and Development - The community offers a structured learning path for newcomers, covering various technical stacks and routes to facilitate entry into the field [8] - Members can engage in discussions and seek advice from industry experts, enhancing their understanding and networking opportunities [12][16]