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README.md

DoorGym

Updates

12/14/2019 Presenting poster at Deep RL workshop at NeurIPS 2019! (paper)

11/3/2019 Now you can choose Baxter as a robot!

8/30/2019 Doorgym-Unity plugin has been released!

8/26/2019 Door opening agent trainer has been released!

8/13/2019 Random Door knob Generator has been released!

8/12/2019 Door knob Dataset (3K knobs for each type) has been released!

DoorGym includes follows.

  • Door knob data set (Pull knob, Lever knob, Round knob 3K each)
  • Robot xml (UC Berkeley's BLUE, Rethinkrobot's Baxter)
  • Random Door world generator
  • Door opening policy trainer
  • Mujoco-Unity Plugin ver. Doorgym

0. Set up the environment

requirement:

  • Ubuntu 16.04 or later (Confirmed only on 16.04)

-Python3.6 or later

-Mujoco

-Mujoco-py

-Gym 0.14.0 or later

-OpenAI Baseline 0.1.6 or later

-Unity3D

etc.

Conda (Anaconda, Miniconda)

Step1. Install Mujoco

  1. Get the license from MuJoCo License page

  2. Download Mujoco2.00 from MuJoCo Product page.

  3. Extruct it and place it in under home/.mujoco, as /mujoco200 (Not mujoco200_linux).

  4. Put your key under both .mujoco/ and .mujoco/mujoco200/bin.

Detailed installation can be checked in following page. https://github.com/openai/mujoco-py

Step2. Set environment var. and install necessary pkgs

sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3 libglew-dev libopenmpi-dev patchelf

Set following path in ~/.bashrc.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/[usr-name]/.mujoco/mujoco200/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-[driver-ver]
export PYTHONPATH=$PYTHONPATH:/home/[use-name]/DoorGym/DoorGym-Unity/python_interface
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

Step3. Clone DoorGym repo and Create conda environment

git clone https://github.com/PSVL/DoorGym
cd ./DoorGym
git submodule init
git submodule update
conda env create -n doorgym -f environment/environment.yml
conda activate doorgym
pip install -r requirements.txt
pip install -r requirements.dev.txt

Step4. Install doorenv (0.0.1)

cd envs
pip install -e .

Step5. Install OpenAI Baselines (0.1.6<)

cd ../..
git clone https://github.com/openai/baselines
cd baselines
pip install -e .

Step6. Import test

Make sure that you can import pytorch and mujoco-py w/o problem.

Python 3.7.4 (default, Aug 13 2019, 20:35:49) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> import mujoco_py

If there is an error while importing mujoco-py, try trouble shooting according to the mujoco-py installation guide

1. Download the randomized door knob dataset

You can download from the following URL (All tar.gz file).

Pull knobs (0.75 GB)

Lever knobs (0.77 GB)

Round knobs (1.24 GB)

  • Extract and place the downloaded door knob dataset under the world_generator/door folder (or make a symlink).
  • Place your favorite robots under the world_generator/robot folder. (Blue robots are there as default)

2. Generate door world (e.g. lever knob and hook arm combination.)

cd [path/to/]DoorGym/world_generator

python world_generator.py --knob-type lever --robot-type floatinghook

Check the model by running the mujoco simulator

cd ~/.mujoco/mujoco200/bin

./simulate [path/to/DoorGym]/DoorGym/world_generator/world/lever_floatinghook/1551848929_lever_floatinghook.xml

More detailed instruction here

3. train the agent on the generated door worlds (e.g. lever knob and hook arm combination.)

Proximal Policy Optimization (PPO) training

python main.py --env-name doorenv-v0 --algo ppo --num-steps 4096 --num-processes 8 --lr 1e-3 --save-name ppo-test --world-path /[abs_path/to/DoorGym/]DoorGym/world_generator/world/pull_floatinghook

Soft Actor Critic (SAC) training

python main.py --env-name doorenv-v0 --algo sac --save-name ppo-test --world-path /[abs_path/to/DoorGym/]DoorGym/world_generator/world/pull_floatinghook

Twin Delayed DDPG (TD3) and Advantage Actor-Critic (A2C) can be used as following arguments

--algo td3 or --algo a2c.

4. Train with vision network estimator

Install Unity3D editor for Linux.

Open the project in DoorGym-Unity Plugin from submodule DoorGym-Unity.

with Unity

python main.py --env-name doorenv-v0 --algo ppo --num-steps 4096 --num-processes 8 --lr 1e-3 --save-name ppo-test --world-path /[abs_path/to/DoorGym/]DoorGym/world_generator/world/pull_floatinghook --visionnet-input --unity

without Unity

python main.py --env-name doorenv-v0 --algo ppo --num-steps 4096 --num-processes 8 --lr 1e-3 --save-name ppo-test --world-path /[abs_path/to/DoorGym/]DoorGym/world_generator/world/pull_floatinghook --visionnet-input

5. Run the policy

python enjoy.py --env-name doorenv-v0 --load-name trained_models/ppo/doorenv-v0_reacher-pull-floatinghook.600.pt --world-path /[abs_path/to/DoorGym/]DoorGym/world_generator/world/pull_floatinghook

Paper

Newer paper with Sim2Real (NeurIPS workshop)

Arxiv paper: https://arxiv.org/abs/1908.01887

Reference

This repo is powered by following excellent repos and software.

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Open source domain randomized door opening training environment

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