collecting demonstrations in the robosuite

[Nimingez]

After collecting demonstrations in the robosuite env, and then converting them using the conversion script provided, there seems to be an issue of action not being in the scale [-1, 1] . I have collected the demonstrations using "collect_human_demonstrations.py". then python conversion/convert_robosuite.py get_dataset_info.py total transitions: 27087 total trajectories: 35 traj length mean: 773.9142857142857 traj length std: 240.8114106727588 traj length min: 362 traj length max: 1490 action min: -7.500000000000007 action max: 7.5

==== Filter Keys ==== filter key train with 32 demos filter key valid with 3 demos

==== Env Meta ==== { "type": 1, "env_name": "Wipe", "env_version": "1.4.1", "env_kwargs": { "env_name": "Wipe", "robots": "UR5e", "controller_configs": { "type": "OSC_POSE", "input_max": 1, "input_min": -1, "output_max": [ 0.05, 0.05, 0.05, 0.5, 0.5, 0.5 ], "output_min": [ -0.05, -0.05, -0.05, -0.5, -0.5, -0.5 ], "kp": 150, "damping_ratio": 1, "impedance_mode": "fixed", "kp_limits": [ 0, 300 ], "damping_ratio_limits": [ 0, 10 ], "position_limits": null, "orientation_limits": null, "uncouple_pos_ori": true, "control_delta": true, "interpolation": null, "ramp_ratio": 0.2 } } }

==== Dataset Structure ==== episode demo_1 with 1129 transitions key: actions with shape (1129, 6) key: states with shape (1129, 13)

Traceback (most recent call last): File "get_dataset_info.py", line 134, in raise Exception("Dataset should have actions in [-1., 1.] but got bounds [{}, {}]".format(action_min, action_max)) Exception: Dataset should have actions in [-1., 1.] but got bounds [-7.500000000000007, 7.5]

the actions not scaled down automatically

[Dhanushvarma]

I think if you clip the actions in the script it will fix the issue, this was also suggested by the authors in one of the previous issues, if I am not wrong.

like so : action = np.clip(action, -1, 1)

[Nimingez]

Have you successfully collected data and used training for robomimic？ @Dhanushvarma

[Dhanushvarma]

What I have noticed is that, the keyboard interface is binary, and training with "keyboard-interface" collected data results in poor training. I think to overcome this, the solution is to use a "SpaceMouse" or some alternative interface.