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| # Improving robot navigation in crowded environments using intrinsic rewards (ICRA 2023) | ||
| # Hyp²Nav: Hyperbolic Planning and Curiosity for Crowd Navigation (IROS 2024) | ||
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| # [Paper](https://arxiv.org/abs/2302.06554) || [Video](https://youtu.be/Ksbok2YM9YY) | ||
| _Guido D'Amely*, Alessandro Flaborea*, Pascal Mettes, Fabio Galasso_ | ||
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| ## Poster | ||
| <img src="doc/icra2023posterA0.png" width="1000" /> | ||
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| The official PyTorch implementation of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024 paper [**Hyp²Nav: Hyperbolic Planning and Curiosity for Crowd Navigation**](https://arxiv.org/abs/2407.13567). | ||
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| ## Abstract | ||
| Autonomous navigation in crowded environments is an open problem with many applications, essential for the coexistence of robots and humans in the smart cities of the future. In recent years, deep reinforcement learning approaches have proven to outperform model-based algorithms. Nevertheless, even though the results provided are promising, the works are not able to take advantage of the capabilities that their models offer. They usually get trapped in local optima in the training process, that prevent them from learning the optimal policy. They are not able to visit and interact with every possible state appropriately, such as with the states near the goal or near the dynamic obstacles. In this work, we propose using intrinsic rewards to balance between exploration and exploitation and explore depending on the uncertainty of the states instead of on the time the agent has been trained, encouraging the agent to get more curious about unknown states. We explain the benefits of the approach and compare it with other exploration algorithms that may be used for crowd navigation. Many simulation experiments are performed modifying several algorithms of the state-of-the-art, showing that the use of intrinsic rewards makes the robot learn faster and reach higher rewards and success rates (fewer collisions) in shorter navigation times, outperforming the state-of-the-art. | ||
| Autonomous robots are increasingly becoming a strong fixture in social environments. Effective crowd navigation requires not only safe yet fast planning, but should also enable interpretability and computational efficiency for working in real-time on embedded devices. In this work, we advocate for hyperbolic learning to enable crowd navigation and we introduce Hyp2Nav. Different from conventional reinforcement learning-based crowd navigation methods, Hyp2Nav leverages the intrinsic properties of hyperbolic geometry to better encode the hierarchical nature of decision-making processes in navigation tasks. We propose a hyperbolic policy model and a hyperbolic curiosity module that results in effective social navigation, best success rates, and returns across multiple simulation settings, using up to 6 times fewer parameters than competitor state-of-the-art models. With our approach, it becomes even possible to obtain policies that work in 2-dimensional embedding spaces, opening up new possibilities for low-resource crowd navigation and model interpretability. Insightfully, the internal hyperbolic representation of Hyp2Nav correlates with how much attention the robot pays to the surrounding crowds, e.g. due to multiple people occluding its pathway or to a few of them showing colliding plans, rather than to its own planned route. | ||
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| ## Setup | ||
| 1. Install [Python-RVO2](https://github.com/sybrenstuvel/Python-RVO2) library | ||
| ### added by aleflabo | ||
| - clone Python-RVO2 and cd in the repo | ||
| - `sudo apt-get install cmake` | ||
| - `cmake build .` | ||
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@@ -27,72 +29,44 @@ Autonomous navigation in crowded environments is an open problem with many appli | |
| - `git clone [email protected]:sybrenstuvel/Python-RVO2.git` (the Python-RVO2 repo) | ||
| - python setup.py install | ||
| 2. Install [socialforce](https://github.com/ChanganVR/socialforce) library | ||
| ### added by aleflabo | ||
| - pip install 'socialforce[test,plot]' | ||
| 3. Install crowd_sim and crowd_nav into pip | ||
| ``` | ||
| pip install -e . | ||
| ``` | ||
| `pip install -e .` | ||
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| ## Getting Started | ||
| This repository are organized in two parts: crowd_sim/ folder contains the simulation environment and crowd_nav/ folder contains codes for training and testing the policies. Details of the simulation framework can be found [here](crowd_sim/README.md). Below are the instructions for training and testing policies, and they should be executed | ||
| inside the crowd_nav/ folder. | ||
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| 1. Train a policy. | ||
| ``` | ||
| python train.py --policy tree-search-rl --output_dir data/tsrl_random_encoder/ --config configs/icra_benchmark/ts_separate_random_encoder.py | ||
| ``` | ||
| with ICM and wandb: | ||
| ``` | ||
| python train.py --policy tree-search-rl --output_dir data/tsrl_random_encoder/ --config configs/icra_benchmark/ts_separate_curiosity.py --gpu --wandb_mode online --wandb_name ICM | ||
| ``` | ||
| ``` | ||
| python train.py --policy tree-search-rl --output_dir data/HyperVnet_HHICM_embDim=from32to2/ --config configs/icra_benchmark/ts_HVNet_Hypercuriosity.py --gpu --wandb_mode online --wandb_name HyperVnet_HHICM_embDim=from32to2 --embedding_dimension=2 | ||
| python train.py --policy tree-search-rl --output_dir data/model_name/ --config configs/icra_benchmark/ts_HVNet_Hypercuriosity.py --gpu --wandb_mode online --wandb_name name_of_the_run --embedding_dimension=[2,128] | ||
| ``` | ||
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| 2. Test policies with 1000 test cases. | ||
| ``` | ||
| python test.py --model_dir data/ICM_reproducing/ | ||
| python test.py --model_dir data/model_name --hyperbolic --embedding_dimension [2,64,128] --gpu --device cuda:0 | ||
| ``` | ||
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| 3. Run policy for one episode and visualize the result. | ||
| ``` | ||
| python test.py --policy tree-search-rl --model_dir data/tsrl_random_encoder/ --phase test --visualize --test_case 0 | ||
| ``` | ||
| ``` | ||
| python test.py --model_dir data/360_HyperVnet_HHICM_embDim=from32to2_human10 --hyperbolic --embedding_dimension 64 --gpu --device cuda:0 | ||
| ``` | ||
| ``` | ||
| python test.py --policy tree-search-rl --model_dir data/360_HyperVnet_HHICM_embDim=from32to2_human10 --phase test --visualize --test_case 50 --hyperbolic --video_file /home/aflabor/HypeRL/crowd_nav/data/360_HyperVnet_HHICM_embDim=from32to2/video_model_HHICM_embDIm=2_test_50.gif --embedding_dimension 2 | ||
| ``` | ||
| visualization | ||
| ``` | ||
| python test.py --policy tree-search-rl --model_dir data/360_HyperVnet_HHICM_embDim=from32to2_human10 --phase test --visualize --test_case 35 --hyperbolic --video_file /home/aleflabo/amsterdam/intrinsic-rewards-navigation/crowd_nav/data/360_HyperVnet_HHICM_embDim=from32to2_human10/video_submission/video_model_HHICM_embDIm=2_test_ --embedding_dimension 2 --human_num 10 | ||
| python test.py --policy tree-search-rl --model_dir data/model_name --phase test --visualize --test_case [1,..,1000] --hyperbolic --video_file /path/to/video/file_path/ --embedding_dimension [2,128] --human_num [5,10] | ||
| ``` | ||
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| Note that in **run_experiments_icra.sh**, some examples of how to train different policies with several exploration algorithms. In **configs/icra_benchmark/**, all the configurations used for testing are shown. | ||
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| ## Improving SG-D3QN | ||
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| <img src="doc/success.gif" width="1000" /> | ||
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| <img src="doc/time.gif" width="1000" /> | ||
| ## Improving other models | ||
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| <img src="doc/models.gif" width="1000" /> | ||
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| ## Acknowledge | ||
| This work is based on [CrowdNav](https://github.com/vita-epfl/CrowdNav) and [RelationalGraphLearning](https://github.com/ChanganVR/RelationalGraphLearning) and [SG-D3QN](https://github.com/nubot-nudt/SG-D3QN). The authors are thankful for their works and for making them available. | ||
| This work is based on [CrowdNav](https://github.com/vita-epfl/CrowdNav) and [RelationalGraphLearning](https://github.com/ChanganVR/RelationalGraphLearning) and [SG-D3QN](https://github.com/nubot-nudt/SG-D3QN) and [SG-D3QN-intrinsic](https://github.com/dmartinezbaselga/intrinsic-rewards-navigation). The authors are thankful for their works and for making them available. | ||
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| ## Citation | ||
| If you use this work in your own research or wish to refer to the paper's results, please use the following BibTeX entries. | ||
| ```bibtex | ||
| @inproceedings{martinez2023improving, | ||
| author = {Martinez-Baselga, Diego and Riazuelo, Luis and Montano, Luis}, | ||
| booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)}, | ||
| title = {Improving robot navigation in crowded environments using intrinsic rewards}, | ||
| year = {2023}, | ||
| pages = {9428-9434}, | ||
| doi = {10.1109/ICRA48891.2023.10160876}, | ||
| url = {https://arxiv.org/abs/2302.06554} | ||
| @inproceedings{damely24hyp2nav, | ||
| author = {Di Melendugno, Guido Maria D'Amely and Flaborea, Alessandro and Mettes, Pascal and Galasso, Fabio}, | ||
| booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, | ||
| title = {Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd Navigation}, | ||
| year = {2024}, | ||
| url = {https://arxiv.org/abs/2407.13567} | ||
| } | ||
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