What is RoboOS 2.0
RoboOS 2.0 is an open-source cross-embodiment brain-cerebellum collaboration framework from Zhipu, designed specifically for embodied intelligence. The framework supports multi-robot collaboration, leverages the integrated MCP protocol and a serverless architecture to achieve lightweight deployment, and lowers the development threshold. The framework includes a cloud computing-based brain module responsible for high-level cognition and multi-agent collaboration; a distributed cerebellum module group dedicated to executing robot-specific skills; and a real-time shared memory mechanism that enhances environmental situational awareness. RoboOS 2.0 provides standardized interfaces that eliminate hardware adaptation differences and uses a skill store to enable intelligent matching and one-click adaptation of robot skill modules, helping robots move from 'standalone intelligence' to 'swarm intelligence'.
Main Features of RoboOS 2.0
- Multi-Robot Collaboration: Supports dynamic allocation and parallel execution of multi-agent tasks, suitable for complex scenarios, and improves task execution efficiency.
- Brain-Cerebellum Collaboration: The brain module handles high-level cognition and multi-agent collaboration, while the cerebellum module is dedicated to executing robot-specific skills, achieving efficient division of labor.
- Lightweight Deployment: Integrates the MCP protocol and a serverless architecture, lowers the development threshold, supports rapid deployment, and simplifies the development process.
- Standardized Interfaces: Provides standardized interfaces that eliminate adaptation differences between different vendors and hardware, and supports one-click adaptation of robot skill modules created by developers worldwide.
- Real-Time Perception and Modeling: Adds a multi-embodiment spatiotemporal memory scene graph sharing mechanism, supports real-time perception and modeling in dynamic environments, and enhances environmental adaptability.
- Task Monitoring and Feedback: Introduces a multi-granularity task monitoring module to achieve closed-loop task feedback, improve the stability and success rate of task execution, and ensure reliable task completion.
Technical Principles of RoboOS 2.0
- Hierarchical Task Decomposition: Decomposes complex tasks step by step into subtasks and dynamically allocates them through a network topology, ensuring efficient task execution.
- Device-Cloud Collaboration：
- Cloud-Based Optimization of Brain Inference Deployment: Leverages the powerful computing capabilities of cloud computing for high-level cognition and multi-agent collaboration.
- Adaptation-Free Registration Mechanism for Cerebellum Skills: Supports rapid deployment and skill registration of cerebellum modules, significantly lowering the development threshold.
- Real-Time Shared Memory Mechanism: Based on a real-time shared memory mechanism, dynamically updates environmental status and task progress, ensuring efficient collaboration among multiple agents.
- Multimodal Data Processing: Supports processing of multimodal data such as high-resolution images, multi-view videos, and scene graphs, improving the model's perception and reasoning capabilities.
- System-Level Optimization: Performs system-level optimization of the end-to-end inference pipeline, achieving an overall performance improvement of 30%, a 27-fold increase in device-cloud communication efficiency, and an average full-link response latency as low as under 3 ms.
RoboOS 2.0 Project Address
- Project Website：https://github.com/FlagOpen/RoboOS
- GitHub Repository：https://github.com/FlagOpen/RoboOS
- arXiv Technical Paper：https://arxiv.org/pdf/2505.03673
Application Scenarios of RoboOS 2.0
- Supermarket Logistics: Multiple robots collaborate to complete cargo handling and shelf organizing tasks, with dynamic path planning and real-time obstacle avoidance, improving logistics efficiency.
- Home Services: Robots assist with household chores, such as cleaning and organizing items, perceiving environmental changes in real time, and adapting to dynamic home scenarios.
- Industrial Production: Multiple robots work collaboratively on production lines to complete parts handling and assembly tasks, improving production efficiency and quality.
- Medical Care: Robots assist with nursing work in hospitals, such as transporting medical supplies and helping patients move, reducing the burden on medical staff.
- Public Facility Maintenance: Robots collaborate to complete cleaning and equipment inspection tasks in public areas, providing real-time status feedback to ensure the normal operation of facilities.