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Introduction\n\nThis repository contains related code for the RSS 2021 [paper](http://www.roboticsproceedings.org/rss17/p053.pdf) \"Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation\" by Muchen Sun, Francesca Baldini, Pete Trautman and Todd Murphey.\n\nIf you use this toolbox, please cite it as below.\n\n```\n@INPROCEEDINGS{SunM-RSS-21, \n    AUTHOR    = {Muchen Sun AND Francesca Baldini AND Peter Trautman AND Todd Murphey}, \n    TITLE     = {{Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation}}, \n    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, \n    YEAR      = {2021}, \n    ADDRESS   = {Virtual}, \n    MONTH     = {July}, \n    DOI       = {10.15607/RSS.2021.XVII.053} \n}\n```\n\n## Interactive Web Demo\n\nYou can play around with DistNav for a one-dimensional demonstration at below (click the image to visit the website).\n\n\u003cp style=\"display: flex; align-items: flex-start;\"\u003e\n  \u003ca href=\"https://maxmsun.github.io/demos/distnav_1d.html\" style=\"margin-right: 10px;\"\u003e\n    \u003cimg src=\"imgs/distnav_web_demo_1.png\" alt=\"Web Demo 1\" width=\"400\"/\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://maxmsun.github.io/demos/distnav_gmm.html\"\u003e\n    \u003cimg src=\"imgs/distnav_web_demo_2.png\" alt=\"Web Demo 2\" width=\"400\"/\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\n## `DistNav` Toolbox\n\nYou can install the crowd navigaton toolbox via `pip install distnav`. The second tutorial below contains examples for how to use this toolbox.\n\n## Tutorials\n\nWe provide a Jupyter notebook tutorials for our algorithm. You can find them under the \"notebooks\" directory.\n - [Tutorial 1: distribution space coupling in one-dimensional space](./notebooks/distnav_1d_tutorial.ipynb): In the first tutorial, we will build from scratch on a one-dimensional two-agents toy example to show how DistNav optimization works. We will show both the analytical solution with numerical integration and approximated solution with sampling and Monte-Carlo integration, and how they can match with\n   each other. You can find a copy of the notebook in Google Colaboratory [**here**](https://colab.research.google.com/github/MurpheyLab/DistNav/blob/master/notebooks/distnav_1d_tutorial.ipynb).\n - [Tutorial 2: distribution space coupling in high-dimensional space using `distnav` toolbox](./notebooks/distnav_traj_tutorial.ipynb): In the second tutorial, we will do distribution space coupling in high-dimensional spaces, to predict the preference distributions over long trajectories for four pedestrians (or robots) walk across each other. We show how to specify initial preference distributions using Gaussian processes regression and how to use our `distnav` toolbox to find optimal\n   preferences via samples. You can find a copy of the notebook in Google Colaboratory [**here**](https://colab.research.google.com/github/MurpheyLab/DistNav/blob/master/notebooks/distnav_traj_tutorial.ipynb).\n\n## Questions?\n\nFeel free to contact Muchen Sun via `muchen@u.northwestern.edu` for any question :)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurpheylab%2Fdistnav","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmurpheylab%2Fdistnav","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurpheylab%2Fdistnav/lists"}