What is OpenMind and How Does It Work?
As stated earlier, OpenMind focuses on creating software that enables intelligent behavior in machines, making human work easier. The company builds tools that will allow robots to interpret data from multiple sources, make decisions using large language models, and perform coordinated actions. For instance, a robot running OpenMind software can access web information, process visual data from cameras, or interact via social media platforms like X.
The platform works by combining AI with physical hardware. Developers configure AI personas that operate in cloud environments or on devices such as TurtleBot 4 robots or humanoid units. These personas use models from providers such as Gemini, Claude, and DeepSeek to handle queries and tasks. In practice, this means a user can instruct a robot to explore a space, assist with educational activities, or post updates online, all of which are mediated by the software stack.
OpenMind's approach emphasizes open-source access, enabling users to modify the code to meet specific needs, such as optimizing for home or office use. The system includes guides for developers covering command-line interface commands, project structures, and steps for integrating new sensors or actions. This modularity supports deployment across varied hardware, reducing dependency on proprietary systems.
The Openmind Application, still in its Beta phase, is regarded as the “Uber For Robots,” allowing users to call for, provide, and evaluate robot services for everyday life. The application promises to turn real-world human contributions into the feedback that trains robots to act like humans.
The OpenMind Ecosystem: OM1 and Fabric
The ecosystem consists of OM1 and Fabric, which together address individual robot functionality and network-level coordination. OM1 handles the core operations on a single device, while Fabric enables interactions among multiple machines.
Fabric functions as a peer-to-peer network that provides machines with verified locations, identities, and coordination protocols. It acts like a combination of GPS for positioning, VPN for secure connections, and a handshake mechanism for trust. Robots join the network with proof of identity and location, allowing them to share data and skills under defined rules for access and provenance. This supports scenarios in which machines form teams to learn from shared experiences or to distribute tasks.
For example, Fabric enables robots to exchange information about languages or skills, allowing one machine to adopt capabilities from another without direct human intervention. The network uses blockchain primitives for on-chain identities and payments, integrating with systems such as Base for badge collections and reputation tracking.
OM1 complements this by providing the runtime environment for agents. It supports middleware like ROS2, Zenoh, and CycloneDDS for communication between components. The architecture uses natural language data buses, making it accessible for debugging and extension.