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Parent(s): 94ffde4
Add robotics task category and project page link (#2)
Browse files- Add robotics task category and project page link (44e7f62e681bc517102ed997ba98227436290617)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: mit
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configs:
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- config_name: default
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data_files:
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# SafeFlowMPC Pretrain Dataset
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---
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license: mit
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task_categories:
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- robotics
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configs:
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- config_name: default
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data_files:
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# SafeFlowMPC Pretrain Dataset
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This repository contains the pretraining dataset for the paper [SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies](https://arxiv.org/abs/2602.12794).
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[**Project Page**](https://www.acin.tuwien.ac.at/en/42d6) | [**Github Code**](https://github.com/TU-Wien-ACIN-CDS/SafeFlowMPC)
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## Dataset Description
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The dataset consists of robotic trajectories used to train a flow matching model for safe trajectory planning. The data was generated using a modified version of the VP-STO planner and is designed for 7-DoF manipulators (specifically tested on KUKA robots).
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This specific dataset is used for the **pretraining** stage of the imitation learning model, which focuses on learning policies without explicit safety considerations. For the finetuning stage with safety considerations, see the [SafeFlowMPC dataset](https://huggingface.co/datasets/ThiesOelerich/SafeFlowMPC).
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It contains:
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- `trajectories`: Sampled robotic joint trajectories.
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- `conditional_data`: Contextual information for the planner (e.g., initial configurations and goal poses).
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## Usage
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This dataset is automatically loaded by the training scripts provided in the official repository. To pretrain the model using this dataset, you can run the following command from the repository:
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```bash
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python train_imitation_learning.py
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```
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## Citation
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If you use this dataset in your research, please cite:
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```bibtex
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@inproceedings{oelerich2026safeflowmpc,
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title={SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies},
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author={Oelerich, Thies and Ebmer, Gerald and Hartl-Nesic, Christian and Kugi, Andreas},
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booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
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year={2026}
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}
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```
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