Collecting human operated datasets in simulation

[mmurray]

Hello,

Can you provide info on how human supervision was provided for the simulated datasets (e.g. lerobot/aloha_sim_transfer_cube_human)? I am starting to setup a similar MuJoCo gym environment for the Stretch (https://github.com/mmurray/gym-stretch) and I would like to collect/train on some human teleop data, but it seems like the current control_robot.py script and data collection examples are setup only for physical robots. Is there a branch somewhere with the code used to collect lerobot/aloha_sim_transfer_cube_human that I can reference?

Thanks!

[Cadene]

Hello @mmurray , we are currently working on adding control_sim_robot.py ;) I let @michel-aractingi comment if he has time.

For lerobot/aloha_sim_transfer_cube_human you might find more info in the original aloha paper: https://arxiv.org/abs/2304.13705

Best

[michel-aractingi]

Hello @mmurray

You can find the control_sim_robot.py script in this branch. Just keep in mind that this is not the final version and plenty of things will change especially with the new refactoring of control_robot.py. I have also only tested it with the mujoco environment in gym_lowcostrobot

The usage is exactly the same as control_robot.py. You only need to define a sim config yaml file in lerobot/configs/env. Here's an example of the one I am using with the gym_lowcostrobot.

# @package _global_

fps: 50

env:
  name: lowcostrobot
  fps: ${fps}
  handle: PushCubeLoop-v0

  gym:
    render_mode: human
    max_episode_steps: 100000

calibration:
  axis_directions: [-1, -1, 1, -1, -1, -1]
  offsets: [0, -0.5, -0.5, 0, -0.5, 0] # factor of pi

eval:
  use_async_envs: false