Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent (2410.06472v1)

Abstract: The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert users. This paper introduces ROSA (Robot Operating System Agent), an AI-powered agent that bridges the gap between the Robot Operating System (ROS) and natural language interfaces. By leveraging state-of-the-art LLMs and integrating open-source frameworks, ROSA enables operators to interact with robots using natural language, translating commands into actions and interfacing with ROS through well-defined tools. ROSA's design is modular and extensible, offering seamless integration with both ROS1 and ROS2, along with safety mechanisms like parameter validation and constraint enforcement to ensure secure, reliable operations. While ROSA is originally designed for ROS, it can be extended to work with other robotics middle-wares to maximize compatibility across missions. ROSA enhances human-robot interaction by democratizing access to complex robotic systems, empowering users of all expertise levels with multi-modal capabilities such as speech integration and visual perception. Ethical considerations are thoroughly addressed, guided by foundational principles like Asimov's Three Laws of Robotics, ensuring that AI integration promotes safety, transparency, privacy, and accountability. By making robotic technology more user-friendly and accessible, ROSA not only improves operational efficiency but also sets a new standard for responsible AI use in robotics and potentially future mission operations. This paper introduces ROSA's architecture and showcases initial mock-up operations in JPL's Mars Yard, a laboratory, and a simulation using three different robots. The core ROSA library is available as open-source.

Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent

The paper presents ROSA, an AI-driven natural language interface designed for robotic systems, which facilitates more intuitive human-robot interaction. Developed by NASA's Jet Propulsion Laboratory, ROSA is built to be compatible with the Robot Operating System (ROS), supporting both ROS1 and ROS2. The primary objective is to lower the technical entry barriers for operating robotic systems by allowing users to issue natural language commands.

Overview and Architecture

ROSA leverages LLMs to interpret user commands and execute them within a robotic environment. It integrates with the ROS ecosystem and uses a modular architecture to facilitate ease of use across different platforms. Core components include:

• Action Space: Defines the set of executable tools that the LLM may invoke, ranging from simple system diagnostics to complex control operations.
• System Prompts: Serve to guide the LLM by providing context about the robot’s identity, capabilities, and limitations.
• Tool Invocation: Enables the model to execute commands by mapping natural language queries to specific actions, ensuring safe and accurate operation through parameter validation and constraint enforcement.

Implementation and Customization

The implementation of ROSA is characterized by a streamlined tool structure, employing Python wrappers for ROS functionalities. It allows for custom tools and prompts, tailoring ROSA to specific robot requirements. Several key design choices underscore the system's flexibility and safety:

• Structured Data Return: Tools return structured outputs, enhancing the LLM's ability to generate accurate responses and reducing the likelihood of incorrect or fabricated information.
• Integration with LangChain: The ReAct framework enables the LLM agent to manage natural language inputs effectively, guiding tool selection and execution while maintaining conversation context.

Demonstrations and Capabilities

ROSA was tested on various robotic systems—NeBula-Spot, EELS, and NVIDIA Nova Carter—each presented with unique operational challenges and environments:

• NeBula-Spot: Deployed in JPL's Mars Yard, showcasing ROSA's ability to execute movement commands and perform scene analysis using VLMs, while effectively managing system diagnostics.
• EELS: Demonstrated in a laboratory environment, emphasizing ROSA's adaptability to robot-specific gaits and use of telemetry to assess and correct navigation tasks.
• NVIDIA Nova Carter: Operated in a simulated environment, highlighting ROSA’s tool customization where it processes LiDAR scans and performs collision checks.

These demonstrations underscore ROSA's ability to offer intuitive operation while ensuring comprehensive situational awareness and safe execution of commands.

Ethical Considerations

Ethical deployment of ROSA involves adherence to Asimov's Three Laws of Robotics, ensuring the agent's actions are safe, transparent, and accountable. The paper stresses the need for:

• Safety Mechanisms: Incorporating redundancy, failover mechanisms, and real-time monitoring to assure the protection of both human and system.
• Privacy Measures: Ensuring data is secure, leveraging local models when necessary to avoid potential breaches associated with using external services.
• Social Impact: Addressing broader societal implications, such as workforce displacement and ethical decision-making, to ensure AI deployment aligns with human values and societal norms.

Conclusion and Future Directions

ROSA represents a meaningful step forward in democratizing access to robotic technologies, allowing users across a spectrum of expertise to control complex systems effectively. The modularity and open-source nature of ROSA facilitate extensibility, laying the groundwork for future enhancements in contextual understanding and multimodal interactions. As robotic applications grow, ROSA’s framework offers a promising pathway for integrating AI in real-world operational settings, fostering ethical and practical use. Future work will focus on refining the agent’s contextual reasoning and exploring novel interaction modalities, advancing ROSA's applicability in diverse mission scenarios.