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OpenMind's OM1 is a modular AI runtime that empowers developers to create and deploy multimodal AI agents across digital environments and physical robots, including Humanoids, Phone Apps, Quadrupeds, educational robots such as TurtleBot 4, and simulators like Gazebo and Isaac Sim. OM1 agents can process diverse inputs like web data, social media, camera feeds, and LIDAR, while enabling physical actions including motion, autonomous navigation, and natural conversations. The goal of OM1 is to make it easy to create highly capable human-focused robots, that are easy to upgrade and (re)configure to accommodate different physical form factors.

Capabilities of OM1

• Modular Architecture: Designed with Python for simplicity and seamless integration.
• Data Input: Easily handles new data and sensors.
• Hardware Support via Plugins: Supports new hardware through plugins for API endpoints and specific robot hardware connections to ROS2, Zenoh, and CycloneDDS. (We recommend Zenoh for all new development).
• Web-Based Debugging Display: Monitor runtime state in WebSim (available at http://localhost:8000/), including user input, and current move/speech/emotion outputs.
• Pre-configured Endpoints: Supports Text-to-Speech, multiple LLMs from OpenAI, xAI, DeepSeek, Anthropic, Meta, Gemini, NearAI, Ollama (local), and multiple Visual Language Models (VLMs) with pre-configured endpoints for each service.

Architecture Overview

Getting Started

If you are new to OM1, this is the fastest path to a successful first run using the spot agent.

Spot uses your webcam to detect objects and sends those observations to the LLM. The model then returns move/speak/emotion outputs, which are visualized in WebSim for debugging.

Spot in this quick start is the default starter configuration to help you understand the OM1 pipeline and WebSim output. WebSim visualizes state updates and does not execute robot hardware actions.

Quick Start (5 Minutes)

1. Install system dependencies.
2. Clone the repository.
3. Add your OpenMind API key.
4. Launch OM1 and verify WebSim output.

Prerequisites

• Python 3.10+
• uv package manager
• Webcam access (recommended if configuring VLM)

Install system packages:

1. Install System Dependencies

For macOS:

brew install portaudio ffmpeg

For Linux:

sudo apt-get update
sudo apt-get install -y portaudio19-dev python3-dev ffmpeg

2. Clone

git clone https://github.com/OpenMind/OM1.git
cd OM1
git submodule update --init
uv venv

3. Configure API Key

Get your API key from OpenMind Portal.

1. Sign in to OpenMind Portal.
2. Open the dashboard and create a new API key.
3. Copy the generated key.

Recommended (shell profile):

export OM_API_KEY="<your_api_key>"

Alternative (project-local):

cp .env.example .env

Then set:

OM_API_KEY=<your_api_key>

in .env.

You can also verify or adjust the fallback key location in config/spot.json5.

4. Launch Spot

uv run src/run.py spot

Verify It Is Working

Open http://localhost:8000/ in your browser.

Your setup is successful if:

• The terminal indicates that WebSim started.
• The WebSim page loads at port 8000.
• You can see Spot state updates (inputs, move/speak/emotion outputs, and latency) in the WebSim UI.

Troubleshooting

• Authentication errors: confirm OM_API_KEY is set and not expired.
• No module errors: run the command with uv run from the repo root.
• Camera access issues: grant terminal/IDE camera permissions in OS settings.
• Address already in use on port 8000: stop the conflicting process or free the port.

OMCU

OMCU is the computational unit for billing on OpenMind's platform. The free plan provides 50 OMCU renewed monthly.

Upgrade your plan here for additional credits.

For more help connecting OM1 to your robot hardware, see getting started.

Note: This quick start uses the Spot starter configuration. WebSim is a runtime visualization and debugging interface, not a hardware control endpoint. For voice interactions, ensure ASR and TTS are configured in config/spot.json5.

What's Next?

• Try out some examples
• Add new inputs and actions.
• Design custom agents and robots by creating your own json5 config files with custom combinations of inputs and actions.
• Change the system prompts in the configuration files (located in /config/) to create new behaviors.

Interfacing with New Robot Hardware

OM1 assumes that robot hardware provides a high-level SDK that accepts elemental movement and action commands such as backflip, run, gently pick up the red apple, move(0.37, 0, 0), and smile. An example is provided in src/actions/move/connector/ros2.py:

...
elif output_interface.action == "shake paw":
    if self.sport_client:
        self.sport_client.Hello()
...

If your robot hardware does not yet provide a suitable HAL (hardware abstraction layer), traditional robotics approaches such as RL (reinforcement learning) in concert with suitable simulation environments (Unity, Gazebo), sensors (such as hand mounted ZED depth cameras), and custom VLAs will be needed for you to create one. It is further assumed that your HAL accepts motion trajectories, provides battery and thermal management/monitoring, and calibrates and tunes sensors such as IMUs, LIDARs, and magnetometers.

OM1 can interface with your HAL via USB, serial, ROS2, CycloneDDS, Zenoh, or websockets. For an example of an advanced humanoid HAL, see Unitree's C++ SDK. Frequently, a HAL, especially ROS2 code, is dockerized and interfaces with OM1 through DDS middleware or websockets.

Recommended Development Platforms

OM1 is developed on:

• Nvidia Thor (running JetPack 7.0) - full support
• Jetson AGX Orin 64GB (running Ubuntu 22.04 and JetPack 6.1) - limited support
• Mac Studio with Apple M2 Ultra with 48 GB unified memory (running MacOS Sequoia)
• Mac Mini with Apple M4 Pro with 48 GB unified memory (running MacOS Sequoia)
• Generic Linux machines (running Ubuntu 22.04)

OM1 should run on other platforms (such as Windows) and microcontrollers such as the Raspberry Pi 5 16GB.

Introduction to BrainPack

From research to real-world autonomy, a platform that learns, moves, and builds with you.

The BrainPack is designed to be mounted directly onto a robot to bring together mapping, object recognition, remote control, and self charging, giving humanoids and quadrupeds what they need to navigate, remember, and act with purpose.

Full Autonomy Guidance

OM1 supports full autonomy for Unitree Go2 and G1 with BrainPack. The following features are supported with BrainPack:

• Navigation - Autonomous path planning and movement.
• SLAM - Simultaneous Localization and Mapping for persistent map-based operation.
• Auto Charging - Automated docking and battery charging workflows.
• Face Detection and Anonymization - Real-time perception and privacy-aware processing.

For more details, see Full Autonomy.

The BrainPack is open-source and you can refer to the guidelines to build your own here.

Simulator Support

OM1 integrates with popular robotics simulators to enable rapid prototyping and testing without physical hardware.

Gazebo

Open source Gazebo support is designed for rapid prototyping, conversational interaction, and behavior testing. Gazebo integration with OM1 is supported for Unitree Go2.

See Gazebo to get started.

Isaac Sim

NVIDIA Isaac Sim support enables physics-accurate simulation with GPU acceleration. Isaac Sim integration with OM1 is supported for Unitree Go2 and G1.

Requires NVIDIA GPU and CUDA support. See Isaac Sim Setup to get started.

Detailed Documentation

More detailed documentation can be accessed at docs.openmind.com.

Contributing

Please make sure to read the Contributing Guide before making a pull request.

License

This project is licensed under the terms of the MIT License, which is a permissive free software license that allows users to freely use, modify, and distribute the software. The MIT License is a widely used and well-established license that is known for its simplicity and flexibility. By using the MIT License, this project aims to encourage collaboration, modification, and distribution of the software.