How to construct an RLDS dataset?

I want to fine-tune the Octo model, but I don't know how to construct my own RLDS dataset. I have already built a reinforcement learning environment using **dm_env**, performed simulation with **Isaac Gym,** and generated an RLDS dataset using **envlogger**.
```
for i in range(FLAGS.num_episodes):
timestep = env.reset()
while True:
# TODO: HOW TO GENERATE ACTION
action = np.random.uniform(-3, 3, size=(9,)).astype(np.float32)
timestep = env.step(action)
iimt_gym_env.render()
iimt_gym_env.close()
```
However, for the **action** part, how should I control the robotic arm to generate grasping motion trajectories? Should it be learned through a reward function, controlled via a keyboard or gamepad, or through motion capture? I'm quite confused about this. Could someone tell me the general solution?