A2L - Active Affordance Learning

Published at ICLR 2020 [OpenReview] [Video] [PDF]

This repo provides a reference implementation for active affordance learning, which can be employed to improve autonomous navigation performance in hazardous environments (demonstrated here using the VizDoom simulator). The repo also contains a variety of convenient utilities that can be re-used in other VizDoom-based projects to improve quality of life.

Setup

This code has been tested on Ubuntu 16.04.

Requirements

1. python >= 3.5
2. keras >= 2.2.0
3. opencv-python >= 3.4.0

Installing Dependencies

1. Install VizDoom simulator into local Python environment.

   # Install ZDoom dependencies
   sudo apt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \
   nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \
   libopenal-dev timidity libwildmidi-dev unzip
   
   # Install Boost libraries
   sudo apt-get install libboost-all-dev
   
   # Install Python 3 dependencies
   sudo apt-get install python3-dev python3-pip
   pip install numpy

2. Install Keras-based segmentation-models library.

   pip install segmentation-models

Downloading Demo Data

In order to download the demo data (which contains train/test maps, pre-computed train beacons, and a pre-trained A2L model), follow the steps below:

1. Download the demo data into the root of the repo and uncompress using the following commands:

   wget https://www.dropbox.com/s/0hn71njit81xiy7/demo_data.tar.gz
   tar -xvzf demo_data.tar.gz

2. Check that the directory structure looks like the following:

   ├── data
   │   ├── beacons
   │   ├── configs
   │   ├── experiments
   │   ├── maps
   │   ├── samples
   │   └── models

Usage

Each executable script included in this repo is prefixed with run in its file name. For a detailed description of all possible configuration arguments, please run with the -h flag.

Generating Partially-Labeled Self-Supervised Samples

The following sequence of commands are used to generate a configurable number of partially-labeled examples of navigability in a self-supervised manner. This should work with any set of maps that are compatible with VizDoom.

1. Generate a set of beacons for each map - which describe valid spawn points from which the agent can start a sampling episode.

   python preprocess/run_beacon_generation.py --wad-dir ../data/maps/train/ --save-dir ../data/beacons/train/

2. Generate a configurable number of self-supervised examples per map.

   python train/run_data_sampling.py --wad-dir ../data/maps/train/ --beacon-dir ../data/beacons/train/ --save-dir ../data/samples/train/ --samples-per-map 500

Training Affordance Segmentation Models

The following command is used to train a ResNet-18-based UNet segmentation model to predict pixel-wise navigability.

1. Train UNet-based segmentation model.

   python train/run_train_model.py --data-dir ../data/samples/train/ --save-dir ../data/models/ --epochs 50 --batch-size 40

Training Segmentation Models with Active Affordance Learning

The following command is used to train a ResNet-18-based UNet segmentation model using active affordance learning. The script alternates between data generation and model training, using trained seed models to actively seek out difficult examples.

1. Train UNet-based segmentation model using active affordance learning.

   python train/run_train_A2L.py --wad-dir ../data/maps/train --beacon-dir ../data/beacons/train --save-dir ../data/models/active --active-iterations 5 --samples-per-map 500 --epochs 50 --batch-size 40

Evaluating Navigation Performance

The following command is used to evaluate the performance of an affordance-based agent in a goal-directed navigation task. It can also be used to evaluate a geometry-based agent by dropping the --model-path argument.

1. Run navigation experiments specified using JSON file.

   python eval/run_eval_navigation.py --wad-dir ../data/maps/test --model-path ../data/models/seg_model.h5 --experiment-path ../data/experiments/navigation/demo.json --iterations 5

Citing

If you've found this code to be useful, please consider citing our paper!

@inproceedings{qi2020learning,
  title={Learning to Move with Affordance Maps},
  author={Qi, William and Mullapudi, Ravi Teja and Gupta, Saurabh and Ramanan, Deva},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2020}
}

Questions

If you have additional questions/concerns, please feel free to reach out to wq@cs.cmu.edu.