Researchers at Carnegie Mellon University have introduced Robot I/O (RIO), an open-source software framework designed to eliminate the extensive infrastructure setup that currently bottlenecks robotics research. Developed within the university’s Robotics Institute, the system provides a unified interface for robot control, data collection, teleoperation, and artificial intelligence deployment. By standardizing these foundational workflows, RIO enables engineers and academics to rapidly adapt AI models across diverse robotic platforms without rebuilding software for each new hardware configuration. Historically, robotics development has been hampered by fragmented, custom-built software stacks that must be recreated when switching between robotic arms, humanoids, or sensor arrays. This duplication of engineering effort often consumes entire semesters before researchers can begin testing novel behaviors. RIO addresses this fragmentation through a modular architecture that treats control components, data pipelines, and deployment tools as swappable building blocks. During initial validation, an undergraduate intern with machine learning experience but no prior robotics background successfully unpacked, configured, and operated a robotic arm using the framework in approximately two hours, highlighting its intended accessibility. The framework’s launch coincides with a shift in the field toward general-purpose AI systems for robot control. Previous software infrastructures were largely engineered before the rise of broad AI models, creating compatibility gaps with modern machine learning workflows. RIO closes this gap by providing shared building blocks for data acquisition and policy training, allowing research groups to reproduce results and share models across laboratories. Students and developers report that the unified pipeline supports simultaneous operation across multiple platforms, including bimanual systems and humanoids, regardless of varying camera or actuator configurations. Beyond academic laboratories, the framework addresses critical scalability challenges in industrial automation. Real-world manufacturing and logistics environments rarely rely on isolated robotic units or fixed sensor setups. A standardized infrastructure reduces the friction involved in transitioning research prototypes into adaptable, production-ready systems. Experts note that reusability across hardware configurations significantly shortens the development cycle and improves consistency in experimental validation. Looking ahead, the development team is expanding hardware support and refining the framework to lower deployment barriers even further. The project’s commercial trajectory is being advanced through Lavoro AI, a startup co-founded by associate research professor Jean Oh, which focuses on streamlining robot deployment and accelerating machine learning integration. Long-term objectives include developing robotics foundation models capable of enabling autonomous task adaptation across platforms and environments. As the field transitions from specialized control logic to generalized AI-driven manipulation, RIO positions itself as a critical infrastructure layer for next-generation robotics research and industrial application.