Update License and Readme

.github/CODEOWNERS

@@ -0,0 +1,4 @@
+# See https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners#codeowners-syntax
+
+# Global owners
+@naokiyokoyama @jiuguangw @bernadettekb

.pre-commit-config.yaml

@@ -35,11 +35,7 @@ repos:
       # The inverse operation is more cumbersome: https://stackoverflow.com/a/54840284
       exclude: |
         (?x)^(
-          docker/ros/web/static/novnc/vendor/browser\-es\-module\-loader/dist/babel\-worker\.js|
-          docker/ros/rootfs/usr/local/share/doro\-lxde\-wallpapers/bg.*\.jpg|
-          docker/ros/web/yarn\.lock|
-          src/modelzoo/detic/lvis_v1_train_cat_info\.json|
-          src/modelzoo/edge_grasp_serve/example_pc\.npy
+          docs/teaser_v1\.jpg
         )$
   -   id: check-toml
   -   id: end-of-file-fixer
@@ -81,3 +77,7 @@ repos:
   rev: 3.0.0
   hooks:
   -  id: forbid-binary
+     exclude: |
+        (?x)^(
+          docs/teaser_v1\.jpg
+        )$

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023-2024 Boston Dynamics AI Institute LLC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,6 +1,41 @@
-# Vision-Language Frontier Maps
+<p align="center">
+  <img src="docs/teaser_v1.jpg" width="700">
+  <h1 align="center">VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation</h1>
+  <h3 align="center">
+    <a href="http://naoki.io/">Naoki Yokoyama</a>, <a href="https://faculty.cc.gatech.edu/~sha9/">Sehoon Ha</a>, <a href="https://faculty.cc.gatech.edu/~dbatra/">Dhruv Batra</a>, <a href="https://www.robo.guru/about.html">Jiuguang Wang</a>, <a href="https://bucherb.github.io">Bernadette Bucher</a>
+  </h3>
+  <p align="center">
+    <a href="http://naoki.io/portfolio/vlfm.html">Project Website</a> , <a href="https://arxiv.org/abs/2312.03275">Paper (arXiv)</a>
+  </p>
+  <p align="center">
+    <a href="https://github.com/bdaiinstitute/vlfm">
+      <img src="https://img.shields.io/badge/License-MIT-yellow.svg" />
+    </a>
+    <a href="https://www.python.org/">
+      <img src="https://img.shields.io/badge/built%20with-Python3-red.svg" />
+    </a>
+    <a href="https://github.com/jiuguangw/Agenoria/actions">
+      <img src="https://github.com/bdaiinstitute/vlfm/actions/workflows/test.yml/badge.svg">
+    </a>
+    <a href="https://github.com/psf/black">
+      <img src="https://img.shields.io/badge/code%20style-black-000000.svg">
+    </a>
+    <a href="https://github.com/astral-sh/ruff">
+      <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json">
+    </a>
+    <a href="https://github.com/python/mypy">
+      <img src="http://www.mypy-lang.org/static/mypy_badge.svg">
+    </a>
+  </p>
+</p>
 
-## 1. Installation
+## :sparkles: Overview
+
+Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors. We introduce a zero-shot navigation approach, Vision-Language Frontier Maps (VLFM), which is inspired by human reasoning and designed to navigate towards unseen semantic objects in novel environments. VLFM builds occupancy maps from depth observations to identify frontiers, and leverages RGB observations and a pre-trained vision-language model to generate a language-grounded value map. VLFM then uses this map to identify the most promising frontier to explore for finding an instance of a given target object category. We evaluate VLFM in photo-realistic environments from the Gibson, Habitat-Matterport 3D (HM3D), and Matterport 3D (MP3D) datasets within the Habitat simulator. Remarkably, VLFM achieves state-of-the-art results on all three datasets as measured by success weighted by path length (SPL) for the Object Goal Navigation task. Furthermore, we show that VLFM's zero-shot nature enables it to be readily deployed on real-world robots such as the Boston Dynamics Spot mobile manipulation platform. We deploy VLFM on Spot and demonstrate its capability to efficiently navigate to target objects within an office building in the real world, without any prior knowledge of the environment. The accomplishments of VLFM underscore the promising potential of vision-language models in advancing the field of semantic navigation.
+
+## :hammer_and_wrench: Installation
+
+### Getting Started
 
 Create the conda environment:
 ```bash
@@ -46,7 +81,9 @@ ln -s ${CONDA_PREFIX}/lib/python3.9/site-packages/nvidia/cusolver/include/*  ${C
 export CUDA_HOME=${CONDA_PREFIX}
 ```
 
-## 2. Downloading the HM3D dataset
+## :dart: Downloading the HM3D dataset
+
+### Matterport
 First, set the following variables during installation (don't need to put in .bashrc):
 ```bash
 MATTERPORT_TOKEN_ID=<FILL IN FROM YOUR ACCOUNT INFO IN MATTERPORT>
@@ -81,14 +118,14 @@ mv objectnav_hm3d_v1 $DATA_DIR/datasets/objectnav/hm3d/v1 &&
 rm objectnav_hm3d_v1.zip
 ```
 
-## 3. Downloading weights for various models
+## :weight_lifting: Downloading weights for various models
 The weights for MobileSAM, GroundingDINO, and PointNav must be saved to the `data/` directory. The weights can be downloaded from the following links:
 - `mobile_sam.pt`:  https://github.com/ChaoningZhang/MobileSAM
 - `groundingdino_swint_ogc.pth`: https://github.com/IDEA-Research/GroundingDINO
 - `yolov7-e6e.pt`: https://github.com/WongKinYiu/yolov7
 - `pointnav_weights.pth`:
 
-## 4. Evaluation within Habitat
+## :arrow_forward: Evaluation within Habitat
 Run the following to evaluate on the HM3D dataset:
 ```bash
 python -m vlfm.run
@@ -97,3 +134,20 @@ To evaluate on MP3D, run the following:
 ```bash
 python -m vlfm.run habitat.dataset.data_path=data/datasets/objectnav/mp3d/val/val.json.gz
 ```
+
+## :newspaper: License
+
+VLFM is released under the [MIT License](LICENSE). This code was produced as part of Naoki Yokoyama's internship at the Boston Dynamics AI Institute in Summer 2023 and is provided "as is" without active maintenance. For questions, please contact [Naoki Yokoyama](http://naoki.io) or [Jiuguang Wang](https://www.robo.guru).
+
+## :black_nib: Citation
+
+If you use VLFM in your research, please use the following BibTeX entry.
+
+```
+@inproceedings{yokoyama2024vlfm,
+  title={VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation},
+  author={Naoki Yokoyama and Sehoon Ha and Dhruv Batra and Jiuguang Wang and Bernadette Bucher},
+  booktitle={International Conference on Robotics and Automation (ICRA)},
+  year={2024}
+}
+```