- Mujoco1.55
- OpenAI Gym
- OpenAI Mujoco-py
├── README.md
├── simulate.py
├── train.py
├── assets
│ ├── koko_full.xml
│ └── STL files
└── koko_gym
└── envs
├── assets
│ ├── koko_reacher.xml
│ └── STL files
├── __init__.py
└── koko_reacher.py- koko_full.xml
MJCF file for the Blue robot. Actuated gripper installed. Having following actuators (joints).
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="base_roll_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="shoulder_lift_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="shoulder_roll_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="elbow_lift_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="elbow_roll_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="wrist_lift_joint" />
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="wrist_roll_joint" />
<position ctrllimited="true" ctrlrange="0 1.05" gear="1.0" joint="robotfinger_actuator_joint" />
<position ctrllimited="true" kp="1.0" ctrlrange="0 1.4" joint="right_fingerlimb_joint" />
<position ctrllimited="true" kp="1.0" ctrlrange="-1.4 0" joint="right_fingertip_joint" />
<position ctrllimited="true" kp="1.0" ctrlrange="0 1.4" joint="left_fingerlimb_joint" />
<position ctrllimited="true" kp="1.0" ctrlrange="-1.4 0" joint="left_fingertip_joint" />
</actuator>Since there are no URDF <mimic> tag equivalent exists in MJCF, the grippers (last four actuators) are actuated by a position control that takes the current robotfinger_actuator_joint angle as a input (fingerlimb_joint moves positive and fingertip_joint goes negative to make the tips paralell each other).
- koko_reacher.py
OpenAI Gym environment for Blue. reacher.step takes 1x8 size action array. The actuator of the gripper joints cannot be controlled respectively but will be controlled at once using robotfinger_actuator_joint's angle as a position input. You can also set your favorite reward signal in a step function.
- koko_reacher.xml
koko_full.xml with target object.
- train.py
Training loop using random controller. Add your favorite algorithm to train the policy.
- simulate.py
Runs the Mujoco-py viewer simulator for 5000 time steps. Use this for test run your trained policy.
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