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