dlsmarta/robosuite_mimicgen_100

Robosuite MimicGen 100 — State-Matched Recorded Dataset

This dataset contains 300 episodes (100 per task) of scripted expert demonstrations for three robosuite manipulation tasks, recorded by replaying mimicgen HDF5 demos in a matched simulation environment.

Tasks

Task	Episodes	Frames	Episode Length	Success Rate
Coffee	100	22,225	210–249 (mean 222)	100/100
Square_D0	100	15,328	135–172 (mean 153)	100/100
Stack	99	10,912	82–131 (mean 110)	96/100
Total	299	48,465		296/300

Note: 1 Stack episode has 0 frames (recording edge case), and 4 Stack demos failed in the original HDF5.

Features

Key	Shape	Type	Description
observation.images.image	(3, 256, 256)	video	Agentview camera
observation.images.wrist_image	(3, 256, 256)	video	Robot wrist (eye-in-hand) camera
observation.state	(8,)	float32	[x, y, z, axisangle_x, axisangle_y, axisangle_z, gripper_q1, gripper_q2]
action	(7,)	float32	[x, y, z, axisangle_x, axisangle_y, axisangle_z, gripper] (OSC_POSE controller, range [-1, 1])

• FPS: 20
• Robot: Panda
• Controller: OSC_POSE (Operational Space Control, 6-DOF pose + gripper)
• Camera resolution: 256×256
• Format: LeRobot v3.0 (video-encoded with SVT-AV1)

How This Dataset Was Created

Source Data

The original demonstrations come from the mimicgen project's core HDF5 datasets, hosted on HuggingFace:

• Repository: amandlek/mimicgen_datasets
• Files used: core/coffee_d0.hdf5, core/square_d0.hdf5, core/stack_d0.hdf5
• Each HDF5 contains 100 scripted expert episodes with states, actions, obs, rewards, and dones.

Why Not Use Existing LeRobot Datasets?

Existing lerobot-format robosuite datasets (e.g., locht131/mimicgen_100_lerobot) were created by replaying a trained BC policy in the environment — not by copying the raw HDF5 scripted actions. This means:

1. Actions differ significantly from the ground-truth scripted demos (different magnitudes, sometimes negative correlations)
2. The BC policy may have used a different env configuration (controller, camera, robosuite version)
3. Training on these datasets and evaluating in a different env configuration leads to 0% success rate even after overfitting

Conversion Process

We recorded a fresh dataset by replaying the exact HDF5 scripted actions in our evaluation environment, using matching initial states:

1. Download the mimicgen core HDF5 files from amandlek/mimicgen_datasets
2. For each demo episode:
   • Create a robosuite env with our exact eval configuration:

     robosuite.make(
         env_name=task,           # e.g. "Coffee"
         robots="Panda",
         has_offscreen_renderer=True,
         use_camera_obs=True,
         use_object_obs=False,
         camera_names=["agentview", "robot0_eye_in_hand"],
         camera_heights=256,
         camera_widths=256,
         control_freq=20,
         controller_configs=robosuite.load_controller_config(default_controller="OSC_POSE"),
     )
     

   • Reset the env, then set the Mujoco simulation state to match the HDF5 demo's initial state:

     sim = env.sim
     state_obj = sim.get_state()
     state_obj.qpos[:] = hdf5_states[0, :sim.model.nq]
     state_obj.qvel[:] = hdf5_states[0, sim.model.nq:sim.model.nq+sim.model.nv]
     sim.set_state(state_obj)
     sim.forward()
     

   • Step through the demo's actions, recording (observation.state, action, images) at each timestep
3. Save as lerobot v3.0 dataset using LeRobotDataset.create() with video encoding

Key insight: Setting sim.set_state() from the HDF5 ensures the scripted actions produce the intended trajectories. Without this, env randomization means the actions are essentially random from the env's perspective.

Conversion Script

The recorder is at scripts/record_from_hdf5.py in the active_learning_vlas repo (robosuite branch).

To reproduce:

python scripts/record_from_hdf5.py \
    --tasks Coffee Square_D0 Stack \
    --episodes_per_task 100 \
    --output_dir data/mimicgen_100_recorded

Requires: robosuite, mimicgen, lerobot, GPU with EGL support for rendering.

Usage with LeRobot

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("dlsmarta/robosuite_mimicgen_100")
print(f"Episodes: {dataset.num_episodes}, Frames: {dataset.num_frames}")

Or in a training config:

dataset:
  repo_id: dlsmarta/robosuite_mimicgen_100
  video_backend: pyav

Citation

If you use this dataset, please also cite the original mimicgen datasets:

@inproceedings{mimicgen2023,
    title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations},
    author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iide and Kannan, Animesh and Sheridan, Jackson and Azara, Andrew and Stone, Travis and Fan, Tse and Shridhar, Mohit and others},
    booktitle={7th Annual Conference on Robot Learning},
    year={2023}
}