组内没有人做具身，导师让我先去踩坑......

Core Viewpoint - The article emphasizes the importance of building a solid foundation in hardware and algorithms for embodied intelligence research, particularly for newcomers in the field [1][12]. Group 1: Research Guidance - For teams without prior experience in embodied intelligence, it is recommended to start with simpler tasks using robotic arms before tackling more complex humanoid robots [1]. - Those with a background in large models should focus on specific downstream tasks like VLA (Visual-Language Action) and VLN (Visual-Language Navigation) to bridge the gap between theory and practical applications [1]. - It is advised to solidify knowledge in reinforcement learning before attempting to develop humanoid robots, as this area is still underdeveloped in the domestic market [1]. Group 2: Community and Resources - The "Embodied Intelligence Knowledge Planet" community serves as a comprehensive platform for sharing knowledge, with nearly 2000 members and aims to grow to 10,000 in two years [3][12]. - The community provides various resources, including technical routes, Q&A, and job opportunities, making it a valuable asset for both beginners and advanced researchers [4][13]. - Members can access a wealth of information, including over 30 technical routes, open-source projects, and data sets related to embodied intelligence [12][26]. Group 3: Technical Insights - The community addresses practical issues such as data collection, model deployment, and the challenges of sim-to-real transitions in robotics [4][5]. - It offers insights into various models and frameworks, including VLA and reinforcement learning, and discusses their applications in robotic tasks [5][6]. - The community also organizes forums and live discussions to keep members updated on the latest trends and challenges in the embodied intelligence field [4][11].