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Bellman-Exploration

This is the code release for our IROS 2023 paper:

Learning-Augmented Model-Based Planning for Visual Exploration
Yimeng Li*, Arnab Debnath*, Gregory J. Stein, Jana Kosecka
George Mason University

Project website

@article{Li2022LearningAugmentedMP,
  title={Learning-Augmented Model-Based Planning for Visual Exploration},
  author={Yimeng Li and Arnab Debnath and Gregory J. Stein and Jana Kosecka},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}

Installation

git clone --branch main https://github.com/yimengli46/bellman-exploration.git
cd  bellman-exploration
mkdir output

Dependencies

We use python==3.7.4.
We recommend using a conda environment.

conda create --name bellman_explore python=3.7.4
source activate bellman_explore

You can install Habitat-Lab and Habitat-Sim following guidance from here.
We recommend installing Habitat-Lab and Habitat-Sim from the source code.
We use habitat==0.2.1 and habitat_sim==0.2.1.
Use the following commands to set it up:

# install habitat-lab
git clone --branch stable https://github.com/facebookresearch/habitat-lab.git
cd habitat-lab
git checkout tags/v0.2.1
pip install -e .

# install habitat-sim
git clone --recurse --branch stable https://github.com/facebookresearch/habitat-sim.git
cd habitat-sim
pip install -r requirements.txt
sudo apt-get update || true
# These are fairly ubiquitous packages and your system likely has them already,
# but if not, let's get the essentials for EGL support:
sudo apt-get install -y --no-install-recommends \
     libjpeg-dev libglm-dev libgl1-mesa-glx libegl1-mesa-dev mesa-utils xorg-dev freeglut3-dev
git checkout tags/v0.2.1
python setup.py install --with-cuda

You also need to install the dependencies:

habitat==0.2.1
habitat-sim==0.2.1
torch==1.8.0
torchvision==0.9.0
matplotlib==3.3.4
networkx==2.6.3
scikit-fmm==2022.3.26
scikit-image
sknw
tensorboardX

Dataset Setup

Download scene dataset of Matterport3D(MP3D) from here.
Upzip the scene data and put it under habitat-lab/data/scene_datasets/mp3d.
You also need to download self-generated task episode data from here
Unzip the episode data and put it under habitat-lab/data/datasets/pointnav/mp3d.
Create a soft link to the data.

cd  bellman-exploration
ln -s habitat-lab/data data

The code requires the datasets in data folder in the following format:

habitat-lab/data
  └── datasets/pointnav/mp3d/v1
       └── temp_train
       └── temp_val
       └── temp_test
  └── scene_datasets/mp3d
        └── 1LXtFkjw3qL
        └── 1pXnuDYAj8r
        └── ....

How to Run?

The code supports
(a) Exploration on MP3D test episodes.
All parameters are managed through the configuration file core/config.py.
When initiating a new task, create a new configuration file and save it in the configs folder.

Running the Demo

Before executing the demo, download the pre-generated scene_maps, scene_floor_heights from here.
Unzip the file and place the folders under bellman-exploration/output.

The trained learning module is available for download here.
Unzip it and place the folders under bellman-exploration/output.

(b) Large-Scale Evaluation
Initiating the evaluation is a straightforward process. Follow these steps:

  1. For desktop evaluation of the Greedy planner, use the following command:
python main_eval.py --config='exp_360degree_Greedy_GT_Potential_1STEP_500STEPS.yaml'

Use the following command for the proposed Bellman planner:

python main_eval.py --config='exp_360degree_DP_GT_Potential_D_Skeleton_Dall_1STEP_500STEPS.yaml'
  1. If you're working with a server equipped with multiple GPUs, choose an alternative configuration file:
python main_eval_multiprocess.py --config='large_exp_360degree_DP_NAVMESH_MAP_UNet_OCCandSEM_Potential_D_Skeleton_Dall_1STEP_500STEPS.yaml'

Feel free to customize configurations to meet your evaluation requirements.
Configuration files are provided in the configs folder, following this naming convention:

  • Files starting with large_exp run complete exploration testing episodes.
  • Files with only exp run the first three episodes of each test scene.
  • Greedy in the title signifies running the greedy planner and FME signifies running frontier-based exploration while DP signifies running the Bellman Equation formulated exploration.
  • NAVMESH uses an oracle mapper, and PCDHEIGHT builds the map on the fly.
  • UNet_OCCandSEM_Potential means the potential is computed by UNet with inputs of both occupancy and semantic maps.
  • GT_Potential means using ground-truth potential values.
  • 500STEPS signifies a maximum of 500 allowed steps.

Train the learning modules

Generate training data

To generate training data, run the following command:

python data_generator_input_partial_map.py

You can customize the training hyperparameters using the configuration file exp_train_input_partial_map_occ_and_sem.yaml.
Here are a few key options:

  • Set the SPLIT parameter to either train or val to generate data for training or validation scenes.
  • Adjust PRED.PARTIAL_MAP.multiprocessing to single or mp for single-threaded or multithreaded generation, respectively.
Train the learning module

Run the following command to initiate training:

python train_UNet_input_partial_map.py

Customize training hyperparameters using the same exp_train_input_partial_map_occ_and_sem.yaml configuration file.
Key options include:

  • Adjust BATCH_SIZE, EPOCHS, and NUM_WORKERS based on your computer hardware and GPU memory.