What is Robot Data Augmentation?

Robot Data Augmentation — generating additional training episodes from existing ones by transforming images, goals or scene configurations.

At the image level the techniques are the familiar ones — crops, color jitter, texture and lighting randomization — and they buy visual robustness cheaply. At the episode level the interesting methods are geometric: MimicGen takes a source demonstration and re-renders it onto new object poses, new receptacles and even a different robot arm, turning tens of human demonstrations into thousands of synthetic episodes.

Relabeling is the other lever. A trajectory that failed to reach its intended goal can be relabeled with the goal it did reach, and a demonstration recorded without language can be given instructions after the fact. Both multiply the useful content of data that was already paid for.

What augmentation cannot do is invent physics. No transformation of a tabletop episode produces the contact dynamics of cloth, and image-level processing cannot create a sensor modality that was not recorded. That is the honest test of whether to buy augmentation or more data: if the missing variation is geometric or visual, augmentation is cheap and effective; if it is contact or sensing, it is neither. Augmentation is normally scoped as part of a collection or conversion engagement rather than bought on its own, because the useful question is what your existing data lacks.

Related terms

  • Sim-to-Real Transfer

    Sim-to-Real Transfer — training a policy in simulation and getting it to work on physical hardware, across the gap between the two.

  • Robot Manipulation Data

    Robot Manipulation Data — episodes of a robot arm moving objects, recorded together with the commands that produced the motion.

  • Robot Data Annotation

    Robot Data Annotation — attaching labels to robot episodes: what the task was, whether it succeeded, and when key events happened.

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