Data Scaling Laws in Imitation Learning for Robotic Manipulation

Understand

Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities.

• In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any environment.
• To this end, we conduct a comprehensive empirical study on data scaling in imitation learning.
• By collecting data across numerous environments and objects, we study how a policy's generalization performance changes with the number of training environments, objects, and demonstrations.