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')
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