RULE

Introducing P37 Neuro, our open-source robot brain at @OntosWorld. For a while, we’ve been working on a question: Can we build a robot brain that keeps working when the body or task changes? P37 Neuro is part of our answer. The system is designed to understand the body it controls, interpret the state of the robot and its environment, follow a task or demonstration, and turn that information into physical actions. A new robot or task shouldn’t always require a completely new model. P37 has an embodiment layer that represents the robot itself, high- and low-level policies for planning and control, and memory that carries context across actions and failures. It also supports reinforcement learning, simulation in MuJoCo and Isaac Lab, and a C++ runtime that connects the learned policy to the physical machine. Around the model, we’ve built the infrastructure for training, evaluation, safety, model releases, fleet feedback, and rollback. The current software path covers P37-E0 through E6. We’ve begun testing the ideas behind the system. A shared model has been trained across different synthetic robot morphologies and evaluated on bodies it had never encountered during training. We’ve also tested demonstration conditioning on unseen tasks, with no fine-tuning during evaluation. For the memory tests, we gave the model information before a simulated failure, then compared its performance when that memory was preserved and when it was removed.