Awesome
Rex: an open-source quadruped robot
The goal of this project is to train an open-source 3D printed quadruped robot exploring
Reinforcement Learning
and OpenAI Gym
. The aim is to let the robot learns domestic and generic tasks in the simulations and then
successfully transfer the knowledge (Control Policies
) on the real robot without any other manual tuning.
This project is mostly inspired by the incredible works done by Boston Dynamics.
Related repositories
rexctl - A CLI application to bootstrap and control Rex robot running the trained Control Policies
.
rex-models - A web URDF visualizer. Collection of Rex robot models.
Rex-gym: OpenAI Gym environments and tools
This repository contains a collection of OpenAI Gym Environments
used to train Rex, the Rex URDF model,
the learning agent implementation (PPO) and some scripts to start the training session and visualise the learned Control Polices
.
This CLI application allows batch training, policy reproduction and single training rendered sessions.
Installation
Create a Python 3.7
virtual environment, e.g. using Anaconda
conda create -n rex python=3.7 anaconda
conda activate rex
PyPI package
Install the public rex-gym
package:
pip install rex_gym
Install from source
Clone this repository and run from the root of the project:
pip install .
CLI usage
Run rex-gym --help
to display the available commands and rex-gym COMMAND_NAME --help
to show the help
message for a specific command.
Use the --arg
flag to eventually set the simulation arguments. For a full list check out the environments parameters.
To switch between the Open Loop
and the Bezier controller (inverse kinematics)
modes, just append either the --open-loop
or --inverse-kinematics
flags.
rex-gym COMMAND_NAME -ik
rex-gym COMMAND_NAME -ol
For more info about the modes check out the learning approach.
Policy player: run a pre-trained agent
To start a pre-trained agent (play a learned Control Policy
):
rex-gym policy --env ENV_NAME
Train: Run a single training simulation
To start a single agent rendered session (agents=1
, render=True
):
rex-gym train --playground True --env ENV_NAME --log-dir LOG_DIR_PATH
Train: Start a new batch training simulation
To start a new batch training session:
rex-gym train --env ENV_NAME --log-dir LOG_DIR_PATH
Robot platform
Mark 1
The robot used for this first version is the Spotmicro made by Deok-yeon Kim.
I've printed the components using a Creality Ender3 3D printer, with PLA and TPU+.
The hardware used is listed in this wiki.
The idea is to extend the robot adding components like a robotic arm on the top of the rack and a LiDAR sensor in the next versions alongside fixing some design issue to support a better (and easier) calibration and more reliable servo motors.
Simulation model
Base model
Rex is a 12 joints robot with 3 motors (Shoulder
, Leg
and Foot
) for each leg.
The robot base
model is imported in pyBullet
using an URDF file.
The servo motors are modelled in the model/motor.py
class.
Robotic arm
The arm
model has the open source 6DOF robotic arm Poppy Ergo Jr equipped on the top of the
rack.
To switch between base
and arm
models use the --mark
flag.
Learning approach
This library uses the Proximal Policy Optimization (PPO)
algorithm with a hybrid policy defined as:
a(t, o) = a(t) + π(o)
It can be varied continuously from fully user-specified to entirely learned from scratch.
If we want to use a user-specified policy, we can set both the lower and the upper bounds of π(o)
to be zero.
If we want a policy that is learned from scratch, we can set a(t) = 0
and give the feedback component π(o)
a wide output range.
By varying the open loop signal and the output bound of the feedback component, we can decide how much user control is applied to the system.
A twofold approach is used to implement the Rex Gym Environments
: Bezier controller
and Open Loop
.
The Bezier controller
implements a fully user-specified policy. The controller uses the Inverse Kinematics
model (see model/kinematics.py
)
to generate the gait.
The Open Loop
mode consists, in some cases, in let the system lean from scratch (setting the open loop component a(t) = 0
) while others
just providing a simple trajectory reference (e.g. a(t) = sin(t)
).
The purpose is to compare the learned policies and scores using those two different approach.
Tasks
This is the list of tasks this experiment want to cover:
- Basic controls:
- Static poses - Frame a point standing on the spot.
- Bezier controller
- Open Loop signal
- Gallop
- forward
- Bezier controller
- Open Loop signal
- backward
- Bezier controller
- Open Loop signal
- Walk
- forward
- Bezier controller
- Open Loop signal
- backward
- Bezier controller
- Open Loop signal
- Turn - on the spot
- Bezier controller
- Open Loop signal
- Stand up - from the floor
- Bezier controller
- Open Loop signal
- Navigate uneven terrains:
- Random heightfield, hill, mount
- Maze
- Stairs
- Open a door
- Grab an object
- Fall recovery
- Reach a specific point in a map
- Map an open space
Terrains
To set a specific terrain, use the --terrain
flag. The default terrain is the standard plane
. This feature is quite useful to
test the policy robustness.
Random heightfield
Use the --terrain random
flag to generate a random heighfield pattern. This pattern is updated at every 'Reset' step.
Hills
Use the --terrain hills
flag to generate an uneven terrain.
Mounts
Use the --terrain mounts
flag to generate this scenario.
Maze
Use the --terrain maze
flag to generate this scenario.
Environments
Basic Controls: Static poses
Goal: Move Rex base to assume static poses standing on the spot.
Inverse kinematic
The gym environment is used to learn how to gracefully assume a pose avoiding too fast transactions.
It uses a one-dimensional action space
with a feedback component π(o)
with bounds [-0.1, 0.1]
.
The feedback is applied to a sigmoid function to orchestrate the movement.
When the --playground
flag is used, it's possible to use the pyBullet UI to manually set a specific pose altering the robot base position
(x
,y
,z
) and orientation (roll
, pitch
, jaw
).
Basic Controls: Gallop
Goal: Gallop straight on and stop at a desired position.
In order to make the learning more robust, the Rex target position is randomly chosen at every 'Reset' step.
Bezier controller
This gym environment is used to learn how to gracefully start the gait and then stop it after reaching the target position (on the x
axis).
It uses two-dimensional action space
with a feedback component π(o)
with bounds [-0.3, 0.3]
. The feedback component is applied to two ramp functions
used to orchestrate the gait. A correct start contributes to void the drift effect generated by the gait in the resulted learned policy.
Open Loop signal
This gym environment is used to let the system learn the gait from scratch. The action space
has 4 dimensions, two for the front legs and feet
and two for the rear legs and feet, with the feedback component output bounds [−0.3, 0.3]
.
Basic Controls: Walk
Goal: Walk straight on and stop at a desired position.
In order to make the learning more robust, the Rex target position is randomly chosen at every 'Reset' step.
Bezier controller
This gym environment is used to learn how to gracefully start the gait and then stop it after reaching the target position (on the x
axis).
It uses two-dimensional action space
with a feedback component π(o)
with bounds [-0.4, 0.4]
. The feedback component is applied to two ramp functions
used to orchestrate the gait. A correct start contributes to void the drift effect generated by the gait in the resulted learned policy.
Forward
Backwards
Open Loop signal
This gym environment uses a sinusoidal trajectory reference to alternate the Rex legs during the gait.
leg(t) = 0.1 cos(2π/T*t)
foot(t) = 0.2 cos(2π/T*t)
The feedback component has very small bounds: [-0.01, 0.01]
. A ramp function are used to start and stop the gait gracefully.
Basic Controls: Turn on the spot
Goal: Reach a target orientation turning on the spot.
In order to make the learning more robust, the Rex start orientation and target are randomly chosen at every 'Reset' step.
Bezier controller
This gym environment is used to optimise the step_length
and step_rotation
arguments used by the GaitPlanner
to implement the 'steer' gait.
It uses a two-dimensional action space
with a feedback component π(o)
with bounds [-0.05, 0.05]
.
Open loop
This environment is used to learn a 'steer-on-the-spot' gait, allowing Rex to moving towards a specific orientation.
It uses a two-dimensional action space
with a small feedback component π(o)
with bounds [-0.05, 0.05]
to optimise the shoulder
and foot
angles
during the gait.
Basic Controls: Stand up
Goal: Stand up starting from the standby position
This environment introduces the rest_postion
, ideally the position assumed when Rex is in standby.
Open loop
The action space
is equals to 1 with a feedback component π(o)
with bounds [-0.1, 0.1]
used to optimise the signal timing.
The signal function applies a 'brake' forcing Rex to assume an halfway position before completing the movement.
Environments parameters
Environment | env flag | arg flag |
---|---|---|
Galloping | gallop | target_position |
Walking | walk | target_position |
Turn | turn | init_orient , target_orient |
Stand up | standup | N.A |
arg | Description |
---|---|
init_orient | The starting orientation in rad. |
target_orient | The target orientation in rad. |
target_position | The target position (x axis). |
Flags | Description |
---|---|
log-dir | The path where the log directory will be created. (Required) |
playground | A boolean to start a single training rendered session |
agents-number | Set the number of parallel agents |
PPO Agent configuration
You may want to edit the PPO agent's default configuration, especially the number of parallel agents launched during the simulation.
Use the --agents-number
flag, e.g. --agents-number 10
.
This configuration will launch 10 agents (threads) in parallel to train your model.
The default value is setup in the agents/scripts/configs.py
script:
def default():
"""Default configuration for PPO."""
# General
...
num_agents = 20
Credits
Papers
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots and all the related papers. Google Brain, Google X, Google DeepMind - Minitaur Ghost Robotics.
Inverse Kinematic Analysis Of A Quadruped Robot
Leg Trajectory Planning for Quadruped Robots with High-Speed Trot Gait
Robot platform v1
Deok-yeon Kim creator of SpotMini.
The awesome Poppy Project.
SpotMicro CAD files: SpotMicroAI community.
Inspiring projects
The kinematics model was inspired by the great work done by Miguel Ayuso.