OpenMind/OM1

What it solves

OM1 provides a modular runtime for developers to build and deploy multimodal AI agents that can operate across both digital environments and physical hardware. It simplifies the process of creating human-focused robots by providing a standardized way to integrate diverse sensors, AI models, and physical actuators, making robots easier to configure and upgrade regardless of their physical form factor.

How it works

OM1 acts as a pipeline that connects data inputs (such as cameras, microphones, LIDAR, and web data) to AI models (LLMs and VLMs) and then to physical or digital actions. It is primarily implemented in Go for high performance and low latency on edge devices. The system uses a plugin-based architecture to support various hardware connections via protocols like Zenoh, ROS2, and CycloneDDS, and provides pre-configured endpoints for major AI services (OpenAI, Anthropic, Gemini, etc.) and local models via Ollama.

Who it’s for

It is designed for robotics developers and AI engineers who want to deploy multimodal agents on humanoids, quadrupeds, phone apps, or in simulators like Gazebo and Isaac Sim.

Highlights

• Multimodal Integration: Processes visual, audio, and text inputs to enable natural conversations and autonomous navigation.
• Broad Hardware Support: Interfaces with physical robots (e.g., Unitree Go2, G1) and simulators through a modular plugin system.
• Performance-Focused: Built in Go to ensure a small memory footprint and low latency for edge deployment.
• Observability: Includes a built-in Prometheus and Grafana stack to monitor real-time pipeline metrics like LLM and ASR latency.
• Full Autonomy Capabilities: Supports SLAM, autonomous path planning, and auto-charging when paired with BrainPack hardware.