We introduce myGym, a toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, so that you can train your network with different robots, in several environments and on various tasks. You can also create a curriculum of tasks with increasing complexity and test your network on them.

From version 3.10 there is SB3 and Gymnasium implemented and there is basic set of protorewards to ccreate any manipulation task from their combination. Their composition is semi automated and will be fully automated in next realese. It is possible to train multiple networks within one task and switch between them based on reward or adaptively. The number of neteworks is specified in config file.

Install myGym 3.10

From myGym 3.10 there is Stable Baseline 3 and Gymnasium. If you want to use old myGym 3.7. with Stable Baselines1 and Gym, switch to branch mygym-3.7

Clone the repository:

git clone https://github.com/incognite-lab/mygym.git

cd mygym

Create Python 3.10 conda env:

conda create -n mygym Python=3.10

conda activate mygym

Install myGym:

pip install -e .

If you face troubles with mpi4py dependency install the lib:

sudo apt install libopenmpi-dev

myGym 3.10 presents

• Atomic rewards

• Protorewards

• Atomic actions

• Easy multi-step task definition

• Nico and Tiago robot support

• Multi-step tasks with custom robots

• Multi-goal rewards for training long horizon

• Automatic tasks checker (oraculum)

• Paralelized training within CPU and GPU on cluster

Overview

Environment	Gym-v0 is suitable both single-step and multi-step manipulation and navigation
Workspaces	Table, Collaborative table, Maze, Vertical maze, Drawer, Darts, Football, Fridge, Stairs, Baskets
Vision	Cartesians, RGB, Depth, Class, Centroid, Bounding Box, Semantic Mask, Latent Vector
Robots	9 robotic arms, 2 dualarms, humanoid
Robot actions	Absolute, Relative, Joints
Objects	54 objects in 5 categories
Tasks	Reach, Press, Switch, Turn, Push, Pick, Place, PicknPlace, Poke,MultiReach, MultiPNP
Randomizers	Light, Texture, Size, Camera position
Baselines	Tensorflow, Pytorch
Physics	Bullet, Mujoco deprecated from version 2.0

Test the environments prior training

You can visualize the virtual gym env prior to the training.

python test.py

There will be the default workspace activated.

Training

Run the default training without specifying the parameters:

python train.py

The default traning is without GUI. You can turn GUI on, or parallelize traning (see train parameters)

Environment

As myGym allows curriculum learning, the workspaces and tasks are concentrated in single gym, so that you can easily transfer the robot. The basic environment is called Gym-v0. There are more gyms for navigation and multi-agent collaboration in preparation.

Robots

Robot	Type	Gripper	DOF	Parameter value
UR-3	arm	no gripper	6	ur3
UR-5	arm	no gripper	6	ur5
UR-10	arm	no gripper	6	ur10
Kuka IIWA	arm	magnetic, gripper	6	kuka
Reachy	arm	passive palm	7	reachy
Leachy	arm	passive palm	7	leachy
Franka-Emica	arm	gripper	7	panda
Jaco arm	arm	two finger	13	jaco
Gummiarm	arm	passive palm	13	gummi
Human Support Robot (HSR)	arm	gripper	7	hsr
ABB Yumi	dualarm	two finger	12	yumi
ReachyLeachy	dualarm	passive palms	14	reachy_and_leachy
Pepper	humanoid	--	20	pepper
Tiago	humanoid	--	19	tiago
Nico	humanoid	--	14	nico

Workspaces

Name	Type	Suitable tasks	Parameter value
Tabledesk	manipulation	Reach,Press, Switch, Turn, PicknPlace	table
Drawer	manipulation	Pick, Place, PicknPlace	drawer
Fridge	manipulation	Push, Pick	fridge
Baskets	manipulation	Throw, Hit	baskets
Darts	manipulation	Throw, Hit	darts
Football	manipulation	Throw, Hit	football
Collaborative table	collaboration	Give, Hold, Move together	collabtable
Vertical maze	planning	--	veticalmaze
Maze	navigation	--	maze
Stairs	navigation	--	stairs

Authors

Incognite lab - CIIRC CTU

Core team:

Michal Vavrecka

Gabriela Sejnova

Megi Mejdrechova

Nikita Sokovnin

Frederik Albl

Sofia Ostapenko

Radoslav Skoviera

Contributors:

Peter Basar, Michael Tesar, Vojtech Pospisil, Jiri Kulisek, Anastasia Ostapenko, Sara Thu Nguyen

Citation

'@INPROCEEDINGS{9643210, author={Vavrecka, Michal and Sokovnin, Nikita and Mejdrechova, Megi and Sejnova, Gabriela},

booktitle={2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)},

title={MyGym: Modular Toolkit for Visuomotor Robotic Tasks},

year={2021}, volume={}, number={}, pages={279-283},

doi={10.1109/ICTAI52525.2021.00046}}'

Paper

myGym: Modular Toolkit for Visuomotor Robotic Tasks