Gymnasium multi-goal environments for goal-conditioned and language-conditioned deep reinforcement learning build with PyBullet
Project description
LANRO (Language Robotics)
LANRO is a platform to study language-conditioned reinforcement learning. It is part of the following publications that introduced the following features:
- a synthetic caretaker providing instructions in hindsight Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics (ICDL 2022, see
icdl2022
branch for old version) - a setup for conversational repair via action corrections Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections (NeurIPS 2022 Workshop LaReL)
Installation
Pip module
pip install lanro-gym
From source
git clone https://github.com/frankroeder/lanro-gym.git
cd lanro-gym/ && pip install -e .
or
# via https
pip install git+https://github.com/frankroeder/lanro-gym.git
# or ssh
pip install git+ssh://git@github.com/frankroeder/lanro-gym.git
Example
import gymnasium as gym
import lanro_gym
env = gym.make('PandaStack2-v0', render=True)
obs, info = env.reset()
terminated = False
while not terminated:
obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
env.close()
Environments
Click here for the environments README
Keyboard and mouse control
It is also possible to manipulate the robot with sliders
python main.py -i --env PandaNLReach2-v0
or your keyboard
python main.py -i --keyboard --env PandaNLReach2-v0
Developers
Running tests
We use pytest.
PYTHONPATH=$PWD pytest test/
Measure the FPS of your system:
PYTHONPATH=$PWD python examples/fps.py
Acknowledgements
This work uses code and got inspired by following open-source projects:
pybullet
Homepage https://pybullet.org/
Source: https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet
License: Zlib
panda-gym
Source: https://github.com/qgallouedec/panda-gym
License: MIT
Changes: The code structure of lanro-gym
contains copies and extensively modified parts of panda-gym
.
Citations
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
@inproceedings{Roder_GroundingHindsight_2022,
title = {Grounding {{Hindsight Instructions}} in {{Multi-Goal Reinforcement Learning}} for {{Robotics}}},
booktitle = {International {{Conference}} on {{Development}} and {{Learning}}},
author = {R{\"o}der, Frank and Eppe, Manfred and Wermter, Stefan},
year = {2022},
pages = {170--177},
publisher = {{IEEE}},
isbn = {978-1-66541-310-7},
}
pybullet
@MISC{coumans2021,
author = {Erwin Coumans and Yunfei Bai},
title = {PyBullet, a Python module for physics simulation for games, robotics and machine learning},
howpublished = {\url{http://pybullet.org}},
year = {2016--2021}
}
panda-gym
@article{gallouedec2021pandagym,
title = {{panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning}},
author = {Gallou{\'e}dec, Quentin and Cazin, Nicolas and Dellandr{\'e}a, Emmanuel and Chen, Liming},
year = 2021,
journal = {4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeurIPS},
}
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