Add paper link, pipeline tag and improve model card
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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license: apache-2.0
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pipeline_tag: robotics
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---
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# MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy
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This repository contains the models presented in the paper [MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy](https://huggingface.co/papers/2511.11931).
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MATT-Diff is a diffusion-policy-based framework for **active multi-target tracking** in partially observable indoor environments. It learns multi-modal action strategies from three expert planners, using a transformer map encoder and an attention-based target encoder over Gaussian beliefs.
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- **Paper:** [https://huggingface.co/papers/2511.11931](https://huggingface.co/papers/2511.11931)
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- **Repository:** [https://github.com/CINAPSLab/MATT-Diff](https://github.com/CINAPSLab/MATT-Diff)
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## Overview
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The policy enables multiple behavior modes for the agent, including exploration, tracking, and target reacquisition, without prior knowledge of the target numbers, states, or dynamics. Trained as a diffusion model, the policy learns to generate multimodal action sequences through a denoising process.
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## Usage
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### Download Checkpoints
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You can download the pretrained checkpoints using the Hugging Face CLI:
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```bash
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huggingface-cli download saidaliu27/MATT-Diff dp/best.pt --local-dir src/run/dp
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huggingface-cli download saidaliu27/MATT-Diff bc/best.pt --local-dir src/run/bc
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```
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### Evaluate
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After downloading the checkpoints and setting up the environment as described in the [GitHub repository](https://github.com/CINAPSLab/MATT-Diff), you can evaluate MATT-Diff:
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```bash
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python -u -m evals.eval_dp
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```
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## Citation
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```bibtex
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@misc{liu2025mattdiffmultimodalactivetarget,
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title={MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy},
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author={Saida Liu and Nikolay Atanasov and Shumon Koga},
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year={2025},
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eprint={2511.11931},
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archivePrefix={arXiv},
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primaryClass={cs.RO},
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url={https://arxiv.org/abs/2511.11931},
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}
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```
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