Bringing the RT-1-X Foundation Model to a SCARA robot
This addresses the problem of adapting robotic models to new robot types for researchers, but it is incremental as it builds on existing foundation models with limited generalization.
This study investigated the generalization of the RT-1-X robotic foundation model to a SCARA robot unseen during training, finding it did not generalize zero-shot but could learn a pickup task through fine-tuning, though object-specific knowledge did not transfer.
Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX. Initial experiments reveal that RT-1-X does not generalize zero-shot to the unseen type of robot. However, fine-tuning of the RT-1-X model by demonstration allows the robot to learn a pickup task which was part of the foundation model (but learned for another type of robot). When the robot is presented with an object that is included in the foundation model but not in the fine-tuning dataset, it demonstrates that only the skill, but not the object-specific knowledge, has been transferred.