Key Points on Viability Overview of the Process Training a quadcopter control model in Unity MLAgents with PyTorch is straightforward, as MLAgents supports ONNX export for inference. The model, mimicking PID controllers (e.g., via reinforcement learning for stability and trajectory), can output actions like thrust adjustments. Embedding into hardware involves converting ONNX to RTL code, […]

I. Executive Synthesis: Feasibility and Strategic Overview A. Assessment of Conceptual Viability The objective of embedding an ONNX model trained within Unity3D’s ML-Agents framework onto a custom Tensor Processing Unit (TPU) architecture, specifically the open-source Tiny-TPU v2, defined by Chisel and synthesized on a low-cost Field-Programmable Gate Array (FPGA), is conceptually sound and technologically viable. […]

Our Mission Robotics AI should be accessible, transparent, and easy to deploy. We publish production-ready ONNX models and reproducible datasets under Apache/Free licenses, accelerating research, prototyping, and real-world deployment. Vision Accurate in the real world—trained on photorealistic, diverse synthetic scenes with domain randomization for edge cases. Lightweight & portable—models deploy on drones, mobile robots, and […]