Open-Embodied

The Open Source Robot Real Time OS for the Embodied Time

embodied by Harvard University, Stanford University, Peking University, BUPT and more

Project Grand Plan

Unifying Robot Interfaces for the Era of Embodied Intelligence

Core of The open embodied project

A Linux-like real-time operating system for Robotics

Member of ROS-Industrial Universal Robots

ROS-Industrial Universal Robots ROS-Industrial Universal Robots

Abstract

In the era of embodied intelligence, where robots are becoming increasingly integrated into our daily lives, the need for a unified robot interface has never been more pressing. Harvard University, Stanford University, Peking University, BUPT and other renowned institutions have jointly initiated the Open-Embodied project, aiming to create a comprehensive platform that encompasses robot operating systems, underlying control algorithms, and an application store for robot applications. This document outlines a grand plan for the Open-Embodied project, including its layered architecture, application-specific modules, and strategies for attracting global robotics researchers and manufacturers to participate.

1. Introduction

The Open-Embodied project is a response to the challenges posed by the proliferation of diverse robot systems and the lack of a standardized interface. By providing a unified platform, the project aims to facilitate the development, deployment, and management of robot applications, enabling seamless communication and interoperability between different robots and systems.

2. Layered Architecture

2.1 Hardware Abstraction Layer (HAL)

The Hardware Abstraction Layer serves as the interface between the robot hardware and the software components. It abstracts the hardware details, such as sensors, actuators, and communication interfaces, providing a standardized set of APIs for the upper layers. This allows developers to write software that is hardware-agnostic, making it easier to port applications across different robot platforms.

2.2 Operating System Layer (OSL)

The Operating System Layer provides a robust and flexible environment for running robot applications. It includes features such as task scheduling, memory management, and inter-process communication. The OSL will be based on a real-time operating system (RTOS) to ensure timely and reliable execution of critical tasks. It will also support multi-threading and multi-processing to take advantage of modern multi-core processors.

2.3 Middleware Layer (ML)

The Middleware Layer acts as a bridge between the OSL and the application layer. It provides a set of services and libraries for common robotics functionalities, such as localization, mapping, navigation, and manipulation. These services are implemented in a modular and reusable manner, allowing developers to easily integrate them into their applications. The ML will also support service discovery and composition, enabling dynamic and flexible application development.

2.4 Application Layer (AL)

The Application Layer is where robot applications are developed and deployed. It provides a user-friendly interface for developers to create, test, and manage their applications. The AL will include an application store, where developers can share and distribute their applications to a global audience. The application store will have features such as version control, user reviews, and ratings to ensure the quality and reliability of the applications.

3. Application-Specific Modules

3.1 Industrial Robotics Module

The Industrial Robotics Module focuses on the needs of industrial automation. It provides tools and libraries for tasks such as assembly, welding, painting, and material handling. The module will support integration with existing industrial control systems, such as PLCs (Programmable Logic Controllers), to enable seamless communication and coordination between robots and other equipment in the factory.

3.2 Service Robotics Module

The Service Robotics Module targets robots used in service industries, such as healthcare, hospitality, and logistics. It includes functionalities for tasks such as patient care, food delivery, and warehouse management. The module will emphasize human-robot interaction, providing natural language processing and gesture recognition capabilities to enable robots to understand and respond to human commands and gestures.

3.3 Agricultural Robotics Module

The Agricultural Robotics Module focuses on robots used in agriculture, such as autonomous tractors, drones for crop monitoring, and robotic harvesters. It provides tools and libraries for tasks such as planting, fertilizing, and harvesting crops. The module will take advantage of advanced sensors and imaging technologies to enable precise and efficient agricultural operations.

3.4 Educational Robotics Module

The Educational Robotics Module is designed for educational institutions and hobbyists. It provides a simple and intuitive interface for learning robotics programming and concepts. The module will include a set of pre-built robot models and simulation environments, allowing users to experiment with different robot configurations and scenarios without the need for physical hardware.

4. Strategies for Attracting Global Participation

4.1 Open Source Collaboration

The Open-Embodied project will be released as an open-source project, allowing researchers and manufacturers from around the world to contribute to its development. By providing a transparent and collaborative development environment, the project can benefit from the diverse expertise and perspectives of the global community. The project will use a version control system, such as Git, to manage the source code and track contributions from different developers.

4.2 Community Building

To foster a vibrant community around the Open-Embodied project, a series of community-building activities will be organized. These activities include online forums, workshops, and hackathons, where researchers and manufacturers can share their ideas, experiences, and best practices. The project will also establish a community governance model to ensure that the community's voice is heard and that the project's direction is aligned with the needs and interests of the community.

4.3 Partnerships and Alliances

The Open-Embodied project will seek partnerships and alliances with leading robotics companies, research institutions, and standards organizations. By collaborating with these partners, the project can gain access to resources, expertise, and market opportunities. For example, the project can partner with robotics companies to integrate their hardware and software solutions into the Open-Embodied platform, or with research institutions to conduct joint research projects on advanced robotics technologies.

4.4 Education and Training

To ensure that researchers and manufacturers have the necessary skills and knowledge to participate in the Open-Embodied project, a comprehensive education and training program will be developed. This program will include online courses, tutorials, and workshops on robotics programming, system integration, and application development. The project will also provide documentation and support materials to help users get started and troubleshoot issues.

4.5 Recognition and Rewards

To incentivize participation in the Open-Embodied project, a recognition and rewards system will be established. This system will recognize and reward developers who make significant contributions to the project, such as developing new features, fixing bugs, or creating high-quality applications. Rewards can include public recognition, prizes, and opportunities to collaborate with leading robotics companies and research institutions.

5. Technical Architecture

5.1 Communication Protocols

The Open-Embodied project will adopt standardized communication protocols to ensure seamless communication between different layers and modules. These protocols will be based on industry standards, such as ROS (Robot Operating System) communication protocols, to ensure compatibility with existing robotics systems. The project will also support custom communication protocols for specific application scenarios, allowing developers to optimize communication performance for their needs.

5.2 Data Management

Effective data management is crucial for the Open-Embodied project, as robots generate large amounts of data during operation. The project will implement a centralized data management system that can store, process, and analyze robot data in real-time. This system will support data sharing and collaboration between different applications and users, enabling researchers and manufacturers to leverage each other's data for better decision-making and innovation.

5.3 Security and Privacy

Security and privacy are important considerations for the Open-Embodied project, especially when dealing with sensitive data and communication between robots and external systems. The project will implement robust security measures, such as encryption, authentication, and access control, to protect against unauthorized access and data breaches. It will also comply with relevant privacy regulations, ensuring that user data is handled in a secure and compliant manner.

6. Conclusion

The Open-Embodied project is a grand initiative aimed at unifying robot interfaces for the era of embodied intelligence. By adopting a layered architecture, application-specific modules, and a series of strategies for attracting global participation, the project can create a vibrant and collaborative ecosystem that drives innovation and advancement in the field of robotics. The project's open-source nature, community-building activities, and education and training programs will ensure that researchers and manufacturers from around the world can contribute to and benefit from the project. With the support of leading robotics companies, research institutions, and standards organizations, the Open-Embodied project has the potential to become the de facto standard for robot interfaces, enabling seamless communication and interoperability between different robot platforms and systems.

Acknowledgements

open-embodied has benefitted from the following projects/resources.

--RROS BUPT RROS

• Evl/xenomai (linux-evl). We learned from evl core how to implement a dual kernel and use dovetail for interrupt virtualization and libevl for user library. Thanks, @Philippe for his genius work and patient explanation in the riot!
• Rust-for-Linux: We use RFL to write RROS in Linux. We ask a lot of questions on RFL Zulip and constructing safety abstractions. Kudos to @ojeda, @Wedson, @Alex, @boqun, @Gary, @Björn in the RFL community for patiently helping us. We hope to bring more safety abstraction back to the RFL in return!
• Muduo: His detailed blog gives us insights for xenomai/evl project.
• All prospective contributors to RROS in the future!