This is the repository for "Towards Optimal Correlational Object Search" (ICRA 2022).
Abstract: In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-todetect objects. In such settings, correlational information can be valuable for planning efficiently. Previous approaches that consider correlational information typically resort to ad-hoc, greedy search strategies. We introduce the Correlational Object Search POMDP (COS-POMDP), which models correlations while preserving optimal solutions with a reduced state space. We propose a hierarchical planning algorithm to scale up COS-POMDPs for practical domains. Our evaluation, conducted with the AI2-THOR household simulator and the YOLOv5 object detector, shows that our method finds objects more successfully and efficiently compared to baselines, particularly for hard-to-detect objects such as srub brush and remote control.
- Paper on arxiv: https://arxiv.org/pdf/2110.09991.pdf
- Presentation video on Youtube: https://www.youtube.com/watch?v=-eehMN6sod8
- More sim demos: https://www.youtube.com/watch?v=RneTq4o0a-A
- Robot demo (on Boston Dynamics Spot): https://www.youtube.com/watch?v=9tCnRTZa9C4&t=7s
Contains two packages: cospomdp and cospomdp_apps. The former defines the COS-POMDP (domain, models, agent) and the latter applies COS-POMDP to specific applications such as AI2-THOR, along with agents that can perform object search in the specific application domain.
@inproceedings{zheng2022towards,
title={Towards Optimal Correlational Object Search,
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
author={Zheng, Kaiyu and Chitnis, Rohan and Sung, Yoonchang and Konidaris, George and Tellex, Stefanie},
year={2022}
}
- Ubuntu 18.04+
- Python 3.8+
If your system does not meet these requirements, you can use Docker. Although we do not provide currently a Dockerfile (help would be appreciated), you could build upon the ubuntu:20.04 image (recommended) or ai2thor-docker (I have not tried), and then do the following instructions inside it. You can set up X11 forwarding to enable GUI inside docker.
Note on dependencies: requirements_snapshot.txt contains a dump of the installed pip packages
at the time when the experiments were conducted. You don't have to install dependencies through
this file. Just follow the steps below. COS-POMDP should still work with
the latest versions of those packages that contain security vulnerability fixes (e.g. for Pillow),
with minor changes when interfacing with those packages, as long as the functionalities of those packages are preserved.
Some dependencies:
- pomdp_py: A framework to build and solve POMDP problems.
- thortils: Utility functions when working with Ai2-THOR (v3.3.4)
- sciex: A framework for scientific experiments
Clone the repo:
git clone [email protected]:zkytony/cos-pomdp.git
After cloning do the following three commands separately.
source setup.bash
source setup.bash -I
source setup.bash -s
It is recommended to use a virtualenv with Python3.8+
To test if it is working: Do pip install pytest and then go to tests/ folder and run
pytest
You are expected to see something like:
-- Docs: https://docs.pytest.org/en/stable/warnings.html
======= 14 passed, 2 skipped, 3 warnings in 43.71s ======
At this point, you should be able to run a basic object search domain with COS-POMDP.
python -m cospomdp_apps.basic.search
A pygame window will be displayed
(Skip this if you are running on a computer connected to a display) If you are running offline, or on a server, make sure there is an x server running. You can do this by:
sudo Xorg -noreset +extension GLX +extension RANDR +extension RENDER -config xorg_conf :0
Note that depending on your local configuration you may want to use something other than :0. If there is already an x server running at :0 but your $DISPLAY shows nothing, you should run it at another display number.
Now, download necessary models by running the following script. This will download three files: yolov5-training-data.zip, yolov5-detectors.zip, corrs.zip, place them in the desired location and decompress them.
# run at repository root
python download.py
Then, main test we will run is
cd tests/thor
python test_vision_detector_search.py
This will run a search trial for AlarmClock in a bedroom scene. When everything works, you may see something like this:
Note that the search process may vary due to random sampling during planning.
The external methods, e.g. SAVN, MJOLNIR, are placed under cos-pomdp/external.
However, for importability, a symbolic link to the cos-pomdp/external/mjolnir
directory is created under cos-pomdp/cospomdp_apps/thor/mjolnir. Please
make sure that link points to the right absolute path on your computer.
For example, you can directly create a new one by:
cd repo/cos-pomdp/cospomdp_apps/thor
ln -sf $HOME/repo/cos-pomdp/external/mjolnir/ mjolnir
Note: setup for SAVN, MJOLNIR etc. were attempted during the project; MJOLNIR can run but does not work well.
You can download experiment results (individual trials) from this Google Drive file link: cospomdp-results-final.zip (45.1MB)
Place this file under the results/ folder and decompress it.
You can gather statistics by running:
cd cospomdp-results-final
python gather_results.py
Refer to the sciex package for more information on the experiment framework.
You can replay a trial by:
python -m cospomdp_apps.thor.replay <trial_name>
The <trial_name> is the name of a directory inside cospomdp-results-final, for example bathroom-FloorPlan421-Candle_000_random#gt.
Note that the dynamically generated topological graph was not saved therefore is not visualized.
Compare with:
- [1] IQA (CVPR'18)
- [2] Visual Semantic Navigation Using Scene Priors (ICRL'19)
- [3] Learning hierarchical relationships for object-goal navigation (CoRL'20)
- [4] Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph (CVPR'21)
| grid size | h_rotate | v_rotate | fov | |
|---|---|---|---|---|
| [1] IQA | 0.25 | 90 | 30 | 60 |
| [2] Scene Priors | 0.25 | 45 | 30 | 90 |
| [3] MJONIR | 0.25 | 45 | 30 | 100 |
| [4] HRL-GRG | 0.25 | 90 | 30 | 90 |
| [5] ALFRED | 0.25 | 90 | 15 | 90 |
| ours | 0.25 | 45 | 30 | 90 |
Constants can be found in cospomdp_apps/thor/constants.py.