Manipulator Agnostic Gripper Control for Reinforcement Learning

Hi Brax Team,

I am unsure rather to ask this in the MuJoCo repository or this, so please excuse me if this question is misplaced :)

I am looking to train an RL algorithm to move and actuate a gripper for manipulation tasks. While i could attach the gripper to a manipulator like the Franka Panda or the UR, I ideally want to train a policy which produces changes in an end-effector poses such that I can handle the manipulator control externally.

I know that in MuJoCo there exists [mocap objects](https://mujoco.readthedocs.io/en/stable/modeling.html#mocap-bodies) which i can weld to objects with free joints and then control the grippers pose using the mocap object.

Using sliding joint actuators i have to also deal with actuator dynamics and their gains which is a non issue with the mocap object, and therefore for me at the moment i would prefer the mocap approach. Is it possible to use the MLP outputs to control the mocap pose?

I minimal example or pseudo code would me immensely appreciated! :)