# Data Augmentation for Manipulation

arXiv: https://arxiv.org/abs/2205.02886 · HTML: https://mlsanity.com/paper/2205.02886/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 16 of 16)

- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning](https://mlsanity.com/paper/2410.24185/) (2024)
- [Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning](https://mlsanity.com/paper/2212.08860/) (2022)
- [SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment](https://mlsanity.com/paper/2410.18907/) (2024)
- [SEIL: Simulation-augmented Equivariant Imitation Learning](https://mlsanity.com/paper/2211.00194/) (2022)
- [Do You Need Proprioceptive States in Visuomotor Policies?](https://mlsanity.com/paper/2509.18644/) (2025)
- [Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning](https://mlsanity.com/paper/2310.18247/) (2023)
- [Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates](https://mlsanity.com/paper/2310.17786/) (2023)
- [Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence](https://mlsanity.com/paper/2402.02701/) (2024)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [CRAFT: Video Diffusion for Bimanual Robot Data Generation](https://mlsanity.com/paper/2604.03552/) (2026)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [Scaling Manipulation Learning with Visual Kinematic Chain Prediction](https://mlsanity.com/paper/2406.07837/) (2024)
- [Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2412.03252/) (2024)
- [Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation](https://mlsanity.com/paper/2508.11204/) (2025)
- [MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2606.24078/) (2026)

## Abstract (arXiv)

The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem. Within computer vision, a common approach to a lack of data is data augmentation. Data augmentation is the process of creating additional training examples by modifying existing ones. However, because the types of tasks and data differ, the methods used in computer vision cannot be easily adapted to manipulation. Therefore, we propose a data augmentation method for robotic manipulation. We argue that augmentations should be valid, relevant, and diverse. We use these principles to formalize augmentation as an optimization problem, with the objective function derived from physics and knowledge of the manipulation domain. This method applies rigid body transformations to trajectories of geometric state and action data. We test our method in two scenarios: 1) learning the dynamics of planar pushing of rigid cylinders, and 2) learning a constraint checker for rope manipulation. These two scenarios have different data and label types, yet in both scenarios, training on our augmented data significantly improves performance on downstream tasks. We also show how our augmentation method can be used on real-robot data to enable more data-efficient online learning.