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From Single-Robot Intelligence to Multi-Robot Collaboration: Architecture and Practice of the AIRS-EAI Full-Stack Open-Source Embodied AI Platform

Currently, embodied AI is facing a critical transition from single-robot functional verification to complex systems that are learnable, transferable, and collaborative. However, issues such as fragmented hardware and software ecosystems and lengthy data closed-loop processes have significantly raised the barriers to development. AIRS-EAI is an open-source embodied AI system initiated by the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) and has joined the Linux Foundation. The project focuses on general-purpose mobile manipulation, dexterous manipulation, and multi-agent collaboration, providing developers with full-stack foundational capabilities ranging from robot system integration, data closed loops, and model training to simulation and evaluation. This presentation will provide an in-depth analysis of the technical evolution of the AIRS-EAI system, with a particular focus on how In-Context Learning (ICL) can be used to enable coordinated collaboration from individual embodied intelligence to collective intelligence, lower the barriers to industrialization, and accelerate the deployment of embodied AI in consumer electronics, laboratory automation, and city-scale scenarios. Outline AIRS-EAI 1.0 Breaking Through: Build a modular integrated foundation of “mobile base + robotic arm” to promote the upgrade of embodied AI in consumer electronics robots. AIRS-EAI 2.0 Deepening: Build a dexterous manipulation operating system for AI scientists to unlock high-precision manipulation and emerging cutting-edge scenarios. AIRS-EAI 3.0 Leaping Forward: Design a multi-agent framework based on In-Context Learning and explore practical applications of collective intelligence collaboration. Key Takeaways Master the modular embodied AI foundation architecture: Understand how different mobile bases, robotic arms, sensors, and computing platforms can achieve rapid, plug-and-play integration under the AIRS-EAI framework. Establish a data-driven R&D closed loop: Learn the standardized engineering process for embodied AI, from data collection, model training, fine-tuning, and evaluation to edge deployment. Gain insights into the frontier of multi-agent collaboration: Develop an in-depth understanding of the application of In-Context Learning in embodied AI and explore the technical path for robots to evolve from single-robot intelligence to collective intelligence. Access a world-class open-source toolchain: Obtain the open-source integration guide for the AIRS-EAI system under the Linux Foundation and leverage the benefits of its ecosystem to reduce the R&D costs of your own projects.