Show HN: Open-source quadruped robot with robotic arm

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The goal of this mission is to prepare an commence-provide 3D printed quadruped robotic exploring
Reinforcement Learning and OpenAI Gym. The goal is to let the robotic learns home and generic projects within the simulations after which
successfully transfer the data (Alter Policies) on the reveal robotic without any various manual tuning.

This mission is mostly inspired by the improbable works completed by Boston Dynamics.

Related repositories

rexctl – A CLI application to bootstrap and regulate Rex running the expert Alter Policies.

rex-cloud – A CLI application to prepare Rex on the cloud.

This repository incorporates a assortment of OpenAI Gym Environments worn to prepare Rex, the Rex URDF model,
the studying agent implementation (PPO) and some scripts to open the coaching session and visualise the learned Alter Polices.
This CLI application enables batch coaching, protection reproduction and single coaching rendered sessions.

Make a Python 3.7 virtual atmosphere, e.g. the utilization of Anaconda

conda make -n rex python=3.7 anaconda
conda spark off rex

PyPI bundle

Install the final public rex-gymnasium bundle:

pip set up rex_gym

Install from provide

Clone this repository and trail from the root of the mission:

pip set up .

Whisk rex-gymnasium --inspire to designate the on hand commands and rex-gymnasium COMMAND_NAME --inspire to expose the inspire
message for a reveal convey.

Utilize the --arg flag to at final living the simulation arguments. For a beefy list test out the environments parameters.

To interchange between the Open Loop and the Bezier controller (inverse kinematics) modes, actual append both the --commence-loop or --inverse-kinematics flags.

rex-gymnasium COMMAND_NAME -ik
rex-gymnasium COMMAND_NAME -ol

For more details about the modes test out the studying arrangement.

Policy player: trail a pre-expert agent

To open a pre-expert agent (play a learned Alter Policy):

rex-gymnasium protection --env ENV_NAME

Tell: Whisk a single coaching simulation

To open a single agent rendered session (brokers=1, render=Factual):

rex-gymnasium prepare --playground Factual --env ENV_NAME --log-dir LOG_DIR_PATH

Tell: Beginning a brand contemporary batch coaching simulation

To open a brand contemporary batch coaching session:

rex-gymnasium prepare --env ENV_NAME --log-dir LOG_DIR_PATH

Mark 1

The robotic worn for this main model is the Spotmicro made by Deok-yeon Kim.

I’ve printed the parts the utilization of a Creality Ender3 3D printer, with PLA and TPU+.

The hardware worn is listed in this wiki.

The premise is to lengthen the robotic adding parts like a robotic arm on the head of the rack and a LiDAR sensor within the following versions alongside
fixing some form advise to enhance an even bigger (and more uncomplicated) calibration and more reliable servo motors.

Depraved model

Rex is a 12 joints robotic with 3 motors (Shoulder, Leg and Foot) for every leg.

The robotic noxious model is imported in pyBullet the utilization of an URDF file.

The servo motors are modelled within the model/motor.py class.

rex bullet

Robotic arm

The arm model has the commence provide 6DOF robotic arm Poppy Ergo Jr geared up on the head of the
rack.

rex arm

To interchange between noxious and arm devices exhaust the --ticket flag.

This library makes exhaust of the Proximal Policy Optimization (PPO) algorithm with a hybrid protection defined as:

a(t, o) = a(t) + π(o)

It’s miles also various persistently from fully person-specified to totally learned from scratch.
If we wish to make exhaust of a person-specified protection, we can living each the lower and the upper bounds of π(o) to be zero.
If we desire a protection that’s learned from scratch, we can living a(t) = 0 and give the suggestions factor π(o) a extensive output vary.

By varying the commence loop signal and the output dash of the suggestions factor, we can advance to a decision how grand person regulate is utilized to the map.

A twofold arrangement is worn to implement the Rex Gym Environments: Bezier controller and Open Loop.

The Bezier controller implements an fully person-specified protection. The controller makes exhaust of the Inverse Kinematics model (search model/kinematics.py)
to generate the gait.

The Open Loop mode consists, in some cases, in let the map lean from scratch (atmosphere the commence loop factor a(t) = 0) whereas others
actual offering a straightforward trajectory reference (e.g. a(t) = sin(t)).

The goal is to study the learned insurance policies and rankings the utilization of these two various arrangement.

Right here is the list of projects this experiment wish to veil:

  1. Total controls:
    1. Static poses – Physique a degree standing on the space.
    • Bezier controller
    • Open Loop signal
    1. Budge
      • forward
      • Bezier controller
      • Open Loop signal
      • backward
      • Bezier controller
      • Open Loop signal
    2. Stride
      • forward
      • Bezier controller
      • Open Loop signal
      • backward
      • Bezier controller
      • Open Loop signal
    3. Flip – on the space
    • Bezier controller
    • Open Loop signal
    1. Stand up – from the floor
    • Bezier controller
    • Open Loop signal
  2. Navigate uneven terrains:
    • Random heightfield, hill, mount
    • Maze
    • Stairs
  3. Open a door
  4. Utilize an object
  5. Tumble restoration
  6. Reach a reveal level in a map
  7. Draw an commence apartment

To living a reveal terrain, exhaust the --terrain flag. The default terrain is the regular aircraft. This selection is moderately worthwhile to
test the protection robustness.

Random heightfield

Utilize the --terrain random flag to generate a random heighfield pattern. This pattern is updated at every ‘Reset’ step.

hf

Hills

Utilize the --terrain hills flag to generate an uneven terrain.

hills

Mounts

Utilize the --terrain mounts flag to generate this scenario.

mounts

Maze

Utilize the --terrain maze flag to generate this scenario.

maze

Total Controls: Static poses

Aim: Switch Rex noxious to bewitch static poses standing on the space.

Inverse kinematic

The gymnasium atmosphere is worn to how that you can well gracefully bewitch a pose warding off too mercurial transactions.
It makes exhaust of a one-dimensional movement apartment with a suggestions factor π(o) with bounds [-0.1, 0.1].
The suggestions is utilized to a sigmoid function to orchestrate the movement.
When the --playground flag is worn, it be conceivable to make exhaust of the pyBullet UI to manually living a reveal pose altering the robotic noxious living
(x,y,z) and orientation (roll, pitch, jaw).

Total Controls: Budge

Aim: Budge straight on and cessation at a desired living.

In speak to form the studying more sturdy, the Rex goal living is randomly chosen at every ‘Reset’ step.

Bezier controller

This gymnasium atmosphere is worn to how that you can well gracefully open the gait after which cessation it after reaching the goal living (on the x axis).
It makes exhaust of two-dimensional movement apartment with a suggestions factor π(o) with bounds [-0.3, 0.3]. The suggestions factor is utilized to two ramp beneficial properties
worn to orchestrate the gait. A appropriate open contributes to void the experience make generated by the gait within the resulted learned protection.

Open Loop signal

This gymnasium atmosphere is worn to let the map learn the gait from scratch. The movement apartment has 4 dimensions, two for the entrance legs and feet
and two for the rear legs and feet, with the suggestions factor output bounds [−0.3, 0.3].

Total Controls: Stride

Aim: Stride straight on and cessation at a desired living.

In speak to form the studying more sturdy, the Rex goal living is randomly chosen at every ‘Reset’ step.

Bezier controller

This gymnasium atmosphere is worn to how that you can well gracefully open the gait after which cessation it after reaching the goal living (on the x axis).
It makes exhaust of two-dimensional movement apartment with a suggestions factor π(o) with bounds [-0.4, 0.4]. The suggestions factor is utilized to two ramp beneficial properties
worn to orchestrate the gait. A appropriate open contributes to void the experience make generated by the gait within the resulted learned protection.

Forward

Backwards

Open Loop signal

This gymnasium atmosphere makes exhaust of a sinusoidal trajectory reference to alternate the Rex legs correct by means of the gait.

leg(t) = 0.1 cos(2π/T*t)
foot(t) = 0.2 cos(2π/T*t)

The suggestions factor has very tiny bounds: [-0.01, 0.01]. A ramp function are worn to open and cessation the gait gracefully.

Total Controls: Flip on the space

Aim: Reach a goal orientation turning on the space.

In speak to form the studying more sturdy, the Rex open orientation and goal are randomly chosen at every ‘Reset’ step.

Bezier controller

This gymnasium atmosphere is worn to optimise the step_length and step_rotation arguments worn by the GaitPlanner to implement the ‘steer’ gait.
It makes exhaust of a two-dimensional movement apartment with a suggestions factor π(o) with bounds [-0.05, 0.05].

Open loop

This atmosphere is worn to learn a ‘steer-on-the-space’ gait, permitting Rex to transferring in direction of a reveal orientation.
It makes exhaust of a two-dimensional movement apartment with a tiny suggestions factor π(o) with bounds [-0.05, 0.05] to optimise the shoulder and foot angles
correct by means of the gait.

Total Controls: Stand up

Aim: Stand up starting from the standby living
This atmosphere introduces the rest_postion, ideally the living assumed when Rex is in standby.

Open loop

The movement apartment is equals to 1 with a suggestions factor π(o) with bounds [-0.1, 0.1] worn to optimise the signal timing.
The signal function applies a ‘brake’ forcing Rex to bewitch an halfway living sooner than finishing the movement.

Atmosphere env flag arg flag
Galloping scamper target_position
Strolling lunge target_position
Flip flip init_orient, target_orient
Stand up standup N.A
arg Description
init_orient The starting orientation in rad.
target_orient The goal orientation in rad.
target_position The goal living (x axis).
Flags Description
log-dir The route the build the log itemizing will most certainly be created. (Required)
playground A boolean to open a single coaching rendered session
brokers-quantity Attach the preference of parallel brokers

PPO Agent configuration

You might well presumably wish to edit the PPO agent’s default configuration, in particular the preference of parallel brokers launched correct by means of
the simulation.

Utilize the --brokers-quantity flag, e.g. --brokers-quantity 10.

This configuration will launch 10 brokers (threads) in parallel to prepare your model.

The default impress is setup within the brokers/scripts/configs.py script:

def default():
    """Default configuration for PPO."""
    # Total
    ...
    num_agents = 20

Papers

Sim-to-Exact: Learning Agile Locomotion For Quadruped Robots and the entire linked 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-Tempo Lumber Gait

Robot platform v1

Deok-yeon Kim creator of SpotMini.

The kindly Poppy Project.

SpotMicro CAD recordsdata: SpotMicroAI community.

Engaging projects

The kinematics model became inspired by the good work completed by Miguel Ayuso.

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