RIO offers an open‑source robot control infrastructure that enables developers to teleoperate robots with minimal setup—often under two hours from unboxing, even without prior robotics expertise. The platform delivers latency up to 4.5× lower than existing frameworks, improving real‑time responsiveness for research and industrial applications. Its upcoming RIO Grande extension adds cross‑embodiment learning, allowing models trained on one robot to be deployed across an entire fleet without re‑engineering the pipeline.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics developers often face high communication latency and complex setup procedures when integrating control frameworks, which slows down experimentation and limits rapid deployment of teleoperated robots. Existing solutions typically require extensive robotics expertise and lengthy configuration, creating barriers for teams without specialized knowledge.
Solution
RIO offers an open‑source robot control infrastructure designed to minimize latency, achieving up to 4.5× lower round‑trip times compared to conventional frameworks. The platform provides a streamlined installation process that enables users to transition from unboxing a robot to full teleoperation in roughly two hours, without needing prior robotics experience. RIO’s modular architecture abstracts hardware specifics, allowing developers to focus on high‑level control logic. An upcoming extension, RIO Grande, introduces cross‑embodiment learning, so models trained on one robot can be deployed across an entire fleet using the same pipeline. By combining low latency, ease of use, and transferable learning, RIO accelerates robot prototyping, testing, and scaling.
Target Audience
RIO targets robotics research labs, product teams, and developers who need fast, low‑latency control without deep robotics expertise, as well as organizations managing fleets of heterogeneous robots.
Features
- Open‑source core library with optimized communication pathways that reduce control loop latency by up to 4.5×
- Plug‑and‑play setup scripts and documentation that enable teleoperation within ~2 hours of hardware receipt
- Hardware‑agnostic abstraction layer supporting a wide range of robot platforms and sensors
- RIO Grande extension for cross‑embodiment model transfer, allowing a single trained policy to run on multiple robot types
- Real‑time telemetry and debugging tools integrated into the control stack for rapid iteration