Robot Foundation Models (RFM)

Robot foundation models are large-scale AI models trained on diverse robotic data that enable robots to understand and perform a wide variety of tasks without task-specific training. These models learn generalizable representations of actions, objects, and environments that transfer across different robot platforms, tasks, and scenarios. Cross-embodiment capability means that knowledge learned on one type of robot (e.g., a robotic arm) can transfer to different robot forms (e.g., a humanoid or mobile robot), dramatically reducing the training required for each new application.

The foundation model approach mirrors the success of large language models in natural language processing, applying similar principles to robotics. By training on massive datasets of robotic demonstrations, sensor data, and task executions, these models develop a general understanding of manipulation, navigation, and interaction that can be adapted to specific tasks with minimal additional training. This enables robots to quickly learn new tasks, adapt to novel situations, and operate in diverse environments. The technology is fundamental to developing general-purpose robots that can perform many different tasks rather than being specialized for single applications, representing a major step toward truly versatile robotic systems.