# DARE **Repository Path**: wrd666/DARE ## Basic Information - **Project Name**: DARE - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-03-24 - **Last Updated**: 2026-03-24 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

DARE: Diffusion Policy to Autonomous Robotics Exploration

[![ICRA 2025](https://img.shields.io/badge/ICRA%202025-Paper-blue?style=flat&logo=ieee)](https://ieeexplore.ieee.org/abstract/document/11128196) [![arXiv](https://img.shields.io/badge/arXiv-2512.02535-red?style=flat&logo=arxiv)](https://arxiv.org/abs/2410.16687) [![Linux platform](https://img.shields.io/badge/Platform-linux--64-orange.svg)](https://ubuntu.com/blog/tag/22-04-lts) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)]()
--- ## Introduction Autonomous robot exploration requires efficient path planning to map unknown environments. While conventional methods are often limited to optimizing based on current beliefs, **DARE (Diffusion Policy for Autonomous Robot Exploration)** leverages the power of generative AI to reason about unknown areas by drawing on learned experiences. DARE is a novel approach that utilizes **diffusion models** trained on expert demonstrations to explicitly generate long-horizon exploration paths. By combining an attention-based encoder with a diffusion policy, DARE learns to recognize potential structures in unknown regions from partial beliefs, enabling it to plan paths that consider these unobserved areas. **Key Features:** * **Generative Path Planning:** Uses diffusion models to explicitly generate efficient exploration paths. * **Expert Demonstrations:** Trained on ground truth optimal demonstrations to learn superior exploration patterns. * **Structure Reasoning:** Capable of reasoning about potential structures in unknown areas based on partial beliefs. * **Robust Performance:** Achieves state-of-the-art performance with strong generalizability in both simulation and real-world scenarios.
--- ## Usage ### Requirements Install the following dependencies in a conda environment as shown below: ```bash git clone https://github.com/marmotlab/DARE.git && cd DARE conda create -n env_dare python=3.12.9 -y conda activate env_dare pip install -e . ``` ### Dataset Collection Modify `dataset_parameter.py` to fit your dataset needs then run dataset collection script: ```bash python dataset_driver.py ``` Dataset will be saved to directory `diffusion_exploration/dataset/name_of_test`. It will include a `data.zarr` directory which contains the dataset and a `gifs` directory. ### Policy Training Copy desired training config file from `diffusion_exploration/diffusion_policy/config`. Modify desired task config file from `diffusion_exploration/diffusion_policy/config/task`. **Note:** You probably should modify the `zarr_path` to change dataset location You can run the training script which requires two arguements: 1. `--config-dir` which is the directory to find the config file 2. `--config-name` which is the name of the config file ```bash python train.py --config-dir=. --config-name=train_exploration_transformer_node_discrete.yaml ``` This will create a directory `diffusion_exploration/data/date/time/name_of_run` ### Evaluation Modify `test_parameter.py` to fit your test needs then run evaluation script: ```bash python test_driver.py ``` Test results will be printed on terminal and saved as a CSV `inference_gifs` directory will be created in `diffusion_exploration/data/date/time/name_of_run`. --- ## Credit If you find this work useful, please consider citing us and the following works: + DARE: Diffusion Policy for Autonomous Robot Exploration ```bibtex @inproceedings{cao2025dare, author={Cao, Yuhong and Lew, Jeric and Liang, Jingsong and Cheng, Jin and Sartoretti, Guillaume}, booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, title={DARE: Diffusion Policy for Autonomous Robot Exploration}, year={2025}, pages={11987-11993}, doi={10.1109/ICRA55743.2025.11128196}} } ``` + ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration ```bibtex @inproceedings{cao2023ariadne, author={Cao, Yuhong and Hou, Tianxiang and Wang, Yizhuo and Yi, Xian and Sartoretti, Guillaume}, booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, title={ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration}, year={2023}, pages={10219-10225}, doi={10.1109/ICRA48891.2023.10160565} } ``` + Deep Reinforcement Learning-based Large-scale Robot Exploration ```bibtex @article{cao2024deepreinforcementlearningbasedlargescale, author={Cao, Yuhong and Zhao, Rui and Wang, Yizhuo and Xiang, Bairan and Sartoretti, Guillaume}, journal={IEEE Robotics and Automation Letters}, title={Deep Reinforcement Learning-Based Large-Scale Robot Exploration}, year={2024}, volume={9}, number={5}, pages={4631-4638}, keywords={Training;Planning;Predictive models;Simultaneous localization and mapping;Trajectory;Three-dimensional displays;Reinforcement learning;Path planning;Robot learning;View Planning for SLAM;reinforcement learning;motion and path planning}, doi={10.1109/LRA.2024.3379804} } ``` + Diffusion policy: Visuomotor policy learning via action diffusion ```bibtex @inproceedings{chi2023diffusionpolicy, title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran}, booktitle={Proceedings of Robotics: Science and Systems (RSS)}, year={2023} } @article{chi2024diffusionpolicy, author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song}, title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, journal = {The International Journal of Robotics Research}, year = {2024}, } ``` We build on the codebase from [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://github.com/marmotlab/large-scale-DRL-exploration) and [Diffusion policy](https://github.com/real-stanford/diffusion_policy). ---