---
license: mit
task_categories:
- robotics
tags:
- reinforcement-learning
- imitation-learning
- robot-manipulation
---

# Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning (ZPRL)

[**Project Page**](https://manutdmoon.github.io/ZPRL/) | [**Paper**](https://huggingface.co/papers/2605.19919) | [**GitHub**](https://github.com/manutdmoon/ZPRL)

This repository contains the datasets used in the paper "Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning".

## Dataset Description

These datasets are prepared for training and evaluating robot manipulation policies using the ZPRL framework. They are based on the [Robomimic Multi-Human (MH) dataset](https://robomimic.github.io/docs/v0.3/datasets/overview.html) and include the following tasks:
- **Can**
- **Square**
- **Transport**

Each dataset consists of 100 trajectories randomly sampled from the original MH dataset. The data has been processed to include:
1. **Image Observations**: Rendered image observations at the resolution required for training.
2. **Absolute Actions**: Delta actions from the original datasets have been converted into absolute actions to facilitate training with flow-matching policies.

### File Structure
The datasets are provided in `.hdf5` format. The directory structure is organized as follows:
```bash
robomimicv030
├── can
│   └── mh
│       └── image_v141_subset_abs.hdf5
├── square
│   └── mh
│       └── image_v141_subset_abs.hdf5
└── transport
    └── mh
        └── image_v141_subset_abs.hdf5
```

## Citation
If you find this dataset or the associated code useful, please consider citing the following paper:
```bibtex
@misc{yu2026zprl,
      title={Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning},
      author={Dongjie Yu and Kun Lei and Zhennan Jiang and Jia Pan and Huazhe Xu},
      year={2026},
      eprint={2605.19919},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2605.19919},
}
```