New 24/11/20: we compared the Guldenring et al. pretrained models (IROS 2020), see here.

NavRep

This is the official repository for the NavRep paper. it contains:

• a simulator aimed at allowing anyone to easy reproduce and improve on the state-of-the-art of RL for robot navigation.
• a suite for unsupervised-learning-assisted RL for robot navigation. It contains tools, datasets, models which allow you to easily reproduce findings from our paper.

Publication

Daniel Dugas, Juan Nieto, Roland Siegwart and Jen Jen Chung, NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments, Pending review, 2020 - pdf

Pre-requisites

Python 3.6

For example, on Ubuntu 20

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt-get install -y python3.6 python3.6-dev
sudo apt-get install -y virtualenv python3-virtualenv

We recommend creating a virtualenv:

virtualenv ~/navrepvenv --python=python3.6
source ~/navrepvenv/bin/activate

rospy:

pip install --extra-index-url https://rospypi.github.io/simple/ rospy rosbag tf tf2_ros

specific versions of Keras, gym (later versions cause errors with some user-facing scripts)

pip install gym==0.15.7 keras==2.3.1

Install

Installing navrep is as simple as

pip install navrep

Try it!

python -m navrep.envs.navreptrainenv

Press w, a, s, d to move around, shift to speed up the simulation.

NavRepSim

NavRep training environment

NavRep testing environment

SOADRL

CrowdMove

Pfeiffer et al.

Reproducing NavRep

The following is an example of how to reproduce the results in the NavRep paper. It is a subset of the full procedure, which can be found in the workflow_navrep.sh file.

make a V dataset

python -m navrep.scripts.make_vae_dataset --environment navreptrain --render

(remove --render argument to speed up data generation)

train V

python -m navrep.scripts.train_vae --environment navreptrain

train M

python -m navrep.scripts.make_rnn_dataset --environment navreptrain
python -m navrep.scripts.train_rnn --environment navreptrain

Play encoded environment

python -m navrep.scripts.play_navreptrainencodedenv --backend VAE_LSTM --encoding V_ONLY

Play dream environment

Train C

python -m navrep.scripts.train_gym_navreptrainencodedenv --backend VAE_LSTM --encoding V_ONLY

Test C

python -m navrep.scripts.cross_test_navreptrain_in_ianenv --backend VAE_LSTM --encoding V_ONLY --render

Run in ROS node

roslaunch launch/sim_and_navrep.launch

Using NavRep with your own models / environments

• Testing your model in the NavRep test environment

Pre-trained NavRep models

all models shown in the paper are available here.

Copy the models from the V, M (modular archs) W (joint and transformer archs) and gym (C module) folders to your home directory according to the following structure:

~/navrep/
  └── models
      ├── gym
      ├── M
      └── V

run the following, and the models inside the ~/navrep directory will be tested in the navrep test environment:

python -m navrep.scripts.cross_test_navreptrain_in_ianenv --backend VAE_LSTM --encoding V_ONLY --render

flags:

--backend

• VAE1D_LSTM (Modular V, M with 1D lidar representation)
• VAE_LSTM (Modular V, M with rings lidar representation)
• VAE1DLSTM (Joint V+M with 1D lidar representation)
• VAELSTM (Joint V+M with rings lidar representation)
• GPT1D (Transformer with 1D lidar representation)
• GPT (Transformer with rings lidar representation)
• E2E1D (End-to-end with 1D lidar representation)
• E2E (End-to-end with rings lidar representation)

--encoding

• V_ONLY (a.k.a only z features)
• M_ONLY (a.k.a only h features)
• VM (a.k.a z+h features)
• VCARCH (for End-to-end only, specifies convolutional architecture)
• CARCH (for End-to-end only, specifies small architecture)

Tested NavRep training regimes

Environments	Modular V, M	Joint V+M	Transformer V+M
NavRep train	✔️	✔️	✔️
NavRep test	✔️	✔️	✔️
SOADRL	✔️	✔️	✔️
Pfeiffer	✔️	✔️	✔️
CrowdMove	➖	➖	➖

Credits

This library was written primarily by Daniel Dugas. The transformer block codes, and vae/lstm code were taken or heavily derived from world models and karpathy's mingpt. We've retained the copyright headers for the relevant files.