ROSA is an AI Agent designed to interact with ROS-based robotics systems using natural language queries.

ROSA is an AI agent that can be used to interact with ROS1 and ROS2 systems in order to carry out various tasks. It is built using the open-source Langchain framework, and can be adapted to work with different robots and environments, making it a versatile tool for robotics research and development.

Features and Roadmap

• Support for both ROS1 and ROS2
• Generate system reports using fuzzy templates
• Read, parse, and summarize ROS log files
• Use natural language to run various ROS commands and tools, for example:
  • "Give me a list of nodes, categorize them into navigation, perception, control, and other"
  • "Show me a list of topics that have publishers but no subscribers"
  • "Set the /velocity parameter to 10"
  • "Echo the /robot/status topic"
  • "What is the message type of the /robot/status topic?"
• Control the TurtleSim robot in simulation using ROSA
• Easily adapt ROSA for your robot by adding new tools and prompts
• Use multi-modal models for vision, scene understanding, and more (in-progress)
• Web-based user interface with support for voice commands (in-progress)
• Text and speech modalities for human-robot interaction (in-progress)

Support for Popular Robots

ROSA already supports any robot built with ROS1 or ROS2, but we are also working on custom agents for some popular robots. These custom agents go beyond the basic ROSA functionality to provide more advanced capabilities and features.

• Custom Agent for the TurtleSim robot (see turtle_agent)
• Custom Agent for NASA JPL's Open Source Rover
• Custom Agent for the TurtleBot
• Custom Agent for the Spot Robot

Contents

• Quick Start
• Changelog
• FAQ
• Contributing Guide
• License
• Support

Quick Start

This guide provides a quick way to get started with ROSA.

Requirements

1. Python 3.9 or higher
2. ROS Noetic (or higher)

Note: ROS Noetic uses Python3.8, but LangChain requires Python3.9 or higher. To use ROSA with ROS Noetic, you will need to create a virtual environment with Python3.9 or higher and install ROSA in that environment.

Setup Instructions

pip3 install jpl-rosa

Usage Examples

from rosa import ROSA

llm = get_your_llm_here()
rosa = ROSA(ros_version=1, llm=llm)
rosa.invoke("Show me a list of topics that have publishers but no subscribers")

TurtleSim Demo

We have included a demo that uses ROSA to control the TurtleSim robot in simulation. To run the demo, you will need to have Docker installed on your machine.

The following video shows ROSA reasoning about how to draw a 5-point star, then executing the necessary commands to do so.

https://github.com/user-attachments/assets/77b97014-6d2e-4123-8d0b-ea0916d93a4e

Setup and run

1. Clone this repository
2. Configure the LLM in src/turtle_agent/scripts/llm.py
3. Run the demo script: ./demo.sh
4. Build and start the turtle agent:

catkin build && source devel/setup.bash && roslaunch turtle_agent agent

5. Run example queries: examples

Adapting ROSA for Your Robot

ROSA is designed to be easily adaptable to different robots and environments. To adapt ROSA for your robot, you can either (1) create a new class that inherits from the ROSA class, or (2) create a new instance of the ROSA class and pass in the necessary parameters. The first option is recommended if you need to make significant changes to the agent's behavior, while the second option is recommended if you want to use the agent with minimal changes.

In either case, ROSA is adapted by providing it with a new set of tools and/or prompts. The tools are used to interact with the robot and the ROS environment, while the prompts are used to guide the agents behavior.

Adding Tools

There are two methods for adding tools to ROSA:

1. Pass in a list of @tool functions using the tools parameter.
2. Pass in a list of Python packages containing @tool functions using the tool_packages parameter.

The first method is recommended if you have a small number of tools, while the second method is recommended if you have a large number of tools or if you want to organize your tools into separate packages.

Hint: check src/turtle_agent/scripts/turtle_agent.py for examples on how to use both methods.

Adding Prompts

To add prompts to ROSA, you need to create a new instance of the RobotSystemPrompts class and pass it to the ROSA constructor using the prompts parameter. The RobotSystemPrompts class contains the following attributes:

• embodiment_and_persona: Gives the agent a sense of identity and helps it understand its role.
• about_your_operators: Provides information about the operators who interact with the robot, which can help the agent understand the context of the interaction.
• critical_instructions: Provides critical instructions that the agent should follow to ensure the safety and well-being of the robot and its operators.
• constraints_and_guardrails: Gives the robot a sense of its limitations and informs its decision-making process.
• about_your_environment: Provides information about the physical and digital environment in which the robot operates.
• about_your_capabilities: Describes what the robot can and cannot do, which can help the agent understand its limitations.
• nuance_and_assumptions: Provides information about the nuances and assumptions that the agent should consider when interacting with the robot.
• mission_and_objectives: Describes the mission and objectives of the robot, which can help the agent understand its purpose and goals.
• environment_variables: Provides information about the environment variables that the agent should consider when interacting with the robot. e.g. $ROS_MASTER_URI, or $ROS_IP.

Example

Here is a quick and easy example showing how to add new tools and prompts to ROSA:

from langchain.agents import tool
from rosa import ROSA, RobotSystemPrompts


@tool
def move_forward(distance: float) -> str:
    """
    Move the robot forward by the specified distance.
    
    :param distance: The distance to move the robot forward.
    """
    # Your code here ...
    return f"Moving forward by {distance} units."


prompts = RobotSystemPrompts(
    embodiment_and_persona="You are a cool robot that can move forward."
)

llm = get_your_llm_here()
rosa = ROSA(ros_version=1, llm=llm, tools=[move_forward], prompts=prompts)
rosa.invoke("Move forward by 2