Skip to main content

A OpenAI Gym Env for Rover with Arm

Project description

A OpenAI Gym Env for Rover with Arm

Project Overview

https://docs.google.com/presentation/d/1NnCJ13qy9eBprIPJsYDFgFuNXPIBvIbRBtx6BVz_si8/edit#slide=id.p3

Task1 - Move the cart near the table and pick up the object in the tray

Reward = 1 (when the object is picked up)

Reward = 0 (else)

Code with Sample Actions

import rover_arm
import gym
env = gym.make('rover-arm-pick-v0', render_mode = 'rgb_array')

observation = env.reset()
done = False

while not done:
    action = env.action_space.sample()
    observation, reward, done, info = env.step(action)
    img = env.render()
    # print(img.shape)
    print(action, observation, reward)
    
print(reward, done, info)

You can try to explore the environment and action space by controlling the bot using Keyboard.

You will have to install the dev version in local, and give keyboard access to terminal or IDE where code is being executed. To install in dev version you could do


pip install 'rover-arm[dev]'

Keyboard Controls

Rover

Up, Down, Left, Right Arrows to steer the Rover.

Arm

A, D -> Move the end-effector in X-axis

W, S -> Move the end-effector in Y-axis

Q, E -> Move the end-effector in Z-axis

-, + -> Open / Close the fingers of the robot arm

Note: W, S are also hot keys to adjust view in pybullet env (so ignore the changes or press again to undo the change.)

Code to control the bot using keyboard in human mode (needs to be run in local)

import rover_arm.keyboard_control as kc

import rover_arm
import gym
env = gym.make('rover-arm-pick-v0', render_mode = 'human')

keyboard_controller = kc.KeyboardAction()
keyboard_controller.start_listening()

observation = env.reset()
done = False

while not done:
    action = keyboard_controller.action
    observation, reward, done, info = env.step(action)
    # print(action, observation, reward)
# print(reward, done, info)

Task2: Pick the object from the closer tray and place it on the distant tray

Use the env "rover-arm-place-v0" for task2


env = gym.make('rover-arm-place-v0')

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rover_arm-1.1.4.tar.gz (2.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

rover_arm-1.1.4-py3-none-any.whl (2.8 MB view details)

Uploaded Python 3

File details

Details for the file rover_arm-1.1.4.tar.gz.

File metadata

  • Download URL: rover_arm-1.1.4.tar.gz
  • Upload date:
  • Size: 2.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.13

File hashes

Hashes for rover_arm-1.1.4.tar.gz
Algorithm Hash digest
SHA256 3b9c994a79042331dd013864c8a7f39da478c796ddb7982f5e14b29296aa3195
MD5 ca97ddb0d82558967c4335fa6d84cbc2
BLAKE2b-256 d0e883822caf5fe4e960b12aa6b441dc601d09ce48d63a71c470a7fb8219222e

See more details on using hashes here.

File details

Details for the file rover_arm-1.1.4-py3-none-any.whl.

File metadata

  • Download URL: rover_arm-1.1.4-py3-none-any.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.13

File hashes

Hashes for rover_arm-1.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 a3ec6334c86de31fb23aa6bb5f9d83958cc570d88b1b48d6af18b31ef47b50ae
MD5 33deecc8c1b2d4cc3bb1f547e34026c6
BLAKE2b-256 85b66d53c7f0b46f1ac45897eaa3f1bee4cea31e863e7e0e766bfdd34499b70c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page