一个P7，从自驾到具身的转行建议......

Core Viewpoint - The article discusses the transition from autonomous driving to embodied intelligence, highlighting similarities in challenges such as data scarcity, algorithm maturity, and deployment strategies [1][6]. Data - The lack of data leads to considerations of real-to-sim and sim-to-real approaches, with suggestions for self-collection methods where robots gather and filter their own data [2]. Algorithms - For commercialization, it is advised to prioritize proven technologies while waiting for newer technologies to mature, emphasizing the use of reinforcement learning methods when applicable [3][4]. Deployment Strategies - Concerns about deployment are minimal, as the industry is adept at lightweight solutions, with expectations for future advancements in data and algorithms [5]. Differences from Autonomous Driving - Unlike autonomous driving, embodied intelligence heavily relies on the physical body, with a greater risk of damage during deployment, necessitating robust safety measures [6]. Career Transition - Transitioning to embodied intelligence is easier for those with relevant experience in autonomous driving or traditional robotics, while newcomers should have a structured learning approach [8]. Community and Resources - The "Embodied Intelligence Knowledge Planet" community offers a comprehensive platform for learning and sharing, with nearly 2000 members and plans to expand to 10,000, providing resources for data collection, algorithm deployment, and job opportunities [10][18]. Research Directions - The community has compiled over 30 technical routes for research, facilitating access to benchmarks and learning pathways for both beginners and advanced practitioners [11][12]. Industry Insights - The community connects members with industry leaders and provides insights into job opportunities, academic advancements, and practical applications in embodied intelligence [18][21]. Resource Compilation - A variety of resources, including open-source projects, datasets, and technical learning routes, are available to support research and development in embodied intelligence [31][37]. Networking Opportunities - Members can engage in discussions, ask questions, and share solutions, fostering a collaborative environment for tackling challenges in the field [78].