/gym-ur5_gripper

Primary LanguagePython

gym-ur5_gripper

This is a gym environment, using ur5 robot and robotiq 3 finger gripper, in mujoco simulator.

The robot xml file is from http://www.mujoco.org/forum/index.php?resources/universal-robots-ur5-robotiq-s-model-3-finger-gripper.22/

This package includes:

  • Robot environments
    • ur5 env
    • gripper env
  • Task environments
    • UR5GripperCatchBall-v0

Overview

install

$ git clone https://github.com/wangcongrobot/gym-ur5_gripper.git
$ cd gym-ur5_gripper
$ virtualenv env --python=python3
$ pip install -e .
$ python gym-ur5_gripper/tests/test_ur5_gripper_env.py

Robot Environments

UR5 Env

  • Action space:

    • Cartesian space: hand 3D pose (x,y,z)
    • Gripper action: open/close

  • Observation space:

    • arm joint positions
    • end-effector pose
    • end-effector velocity

Task Environments

UR5 Reach Env

Observation space

  • UR5's observation space
  • plus object position and orientation

Reward function

In this environment, the reward function is given by:

  • the distance between the end-effector and the desired position
  • plus a bonus when the end-effector is close to the desired position

Here is the code used to compute the reward function:

Gripper

The Robotiq 3 finger gripper has 11 dof, the control mode includes torque control, position control and so on.

We use position control, and change the 11-dof joint control into a 1-dof open/close action.

def gripper_format_action(self, action):
    """ Given (-1,1) abstract control as np-array return the (-1,1) control signals
    for underlying actuators as 1-d np array
    Args:
        action: 1 => open, -1 => closed
    """
    movement = np.array([0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1])
    return -1 * movement * action

P.S.: 1 => open, 0 => close, (0,1) => grasp