---
license: mit
task_categories:
- robotics
- reinforcement-learning
tags:
- robotics
- world-models
- imitation-learning
- robomimic
- mimicgen
- libero
- robocasa
- dexmimicgen
- robosuite
- lance
- multi-view
size_categories:
- 1M<n<10M
---

# robosuite + LeRobot manipulation demonstrations (Lance, 3-view)

**239 manipulation datasets** in the
[Lance](https://lancedb.github.io/lance/) format, across seven collections:

| directory | datasets | contents |
|---|---|---|
| `libero/` | 130 | [LIBERO](https://libero-project.github.io)-130 tasks (90 + 10 + object + spatial + goal) |
| `robocasa/` | 65 | [RoboCasa](https://robocasa.ai) atomic kitchen tasks |
| `mimicgen/` | 26 | [MimicGen](https://mimicgen.github.io) task/level variants |
| `dexmimicgen/` | 9 | [DexMimicGen](https://dexmimicgen.github.io) bimanual dexterous tasks |
| `robomimic/` | 5 | [robomimic](https://robomimic.github.io) proficient-human tasks |
| `ogbench/` | 2 | `ogb_cube_single`, `ogb_scene_single` |
| `molmo_obj_mug/` | 2 | `molmo_obj_mug`, `molmo_obj_mug_videos` |

The five replay-rendered collections hold ≈ **14.11M frames**. Every dataset is
a single-version `<name>.lance/` directory under its collection.

These are **source demonstrations, not policy rollouts**. robomimic, MimicGen,
LIBERO and DexMimicGen were replayed in robosuite from recorded MuJoCo states and
re-rendered to 224×224 from three camera views. RoboCasa ships as pre-rendered
[LeRobot](https://github.com/huggingface/lerobot) videos (v0.2), so its three
views are decoded directly from those videos (no replay) and resized to 224×224
for consistency.

## Cameras (3 views)

Every step carries three JPEG-encoded 224×224 RGB views, in a
two-exterior-plus-wrist layout:

| column | robomimic / MimicGen / LIBERO | RoboCasa | role |
|---|---|---|---|
| `pixels` | `agentview` | `robot0_agentview_left` | primary exterior |
| `sideview` | `sideview` | `robot0_agentview_right` | second exterior |
| `robot0_eye_in_hand` | wrist | `robot0_eye_in_hand` | in-hand / eye-in-hand |

The PickPlace-family envs (`mimicgen_pick_place_d0`, `robomimic_can`) do not define
a `sideview` camera natively, so the standard robosuite world-level `sideview` is
injected at render time. All replay-rendered datasets therefore share the same
three views; RoboCasa's three come from its native left/right/wrist LeRobot videos.

## Schema

All datasets share the framework columns below:

| column | type | meaning |
|---|---|---|
| `episode_idx` | int32 | episode index |
| `step_idx` | int32 | step within episode |
| `pixels` / `sideview` / `robot0_eye_in_hand` | binary (JPEG) | the three 224×224 RGB views |
| `action` | float[] | controller action (robosuite 7-dim single-arm / 14-dim bimanual; RoboCasa 12-dim) |
| `state` | float[] | per-step state vector |
| `reward` | float[1] | task reward (sparse) |
| `terminated` / `truncated` / `success` | float[1] | episode-end / success flags |

Replay-rendered sources (robomimic, MimicGen, LIBERO) additionally carry
`qpos` / `qvel` (MuJoCo generalized position / velocity), `render_time`, `id`,
and **each source's own named low-dim observation keys, kept verbatim** — e.g.
robosuite's `robot0_eef_pos`, `object`, …; LIBERO's `ee_pos`, `ee_ori`,
`gripper_states`, `joint_states`. There `state` is the flattened MuJoCo state used
to drive replay.

**RoboCasa differs**: it is derived from LeRobot recordings, not a MuJoCo replay,
so it has no `qpos`/`qvel`/sim-state. Its `state` is the LeRobot 16-dim
`observation.state` proprioception and `action` is the 12-dim LeRobot action.
Each source keeps its own low-dim observation names, so they vary by collection.

## Datasets

### robomimic — proficient-human (`ph`, low_dim source), re-rendered

| dataset | frames |
|---|---|
| `robomimic_can` | 23,207 |
| `robomimic_lift` | 9,666 |
| `robomimic_square` | 30,154 |
| `robomimic_tool_hang` | 95,962 |
| `robomimic_transport` | 93,752 |

### MimicGen — reset-distribution levels

| dataset | frames | | dataset | frames |
|---|---|---|---|---|
| `mimicgen_coffee_d0` | 223,130 | | `mimicgen_square_d0` | 153,477 |
| `mimicgen_coffee_d1` | 224,403 | | `mimicgen_square_d1` | 152,400 |
| `mimicgen_coffee_d2` | 224,204 | | `mimicgen_square_d2` | 153,112 |
| `mimicgen_coffee_preparation_d0` | 689,273 | | `mimicgen_stack_d0` | 107,590 |
| `mimicgen_coffee_preparation_d1` | 687,674 | | `mimicgen_stack_d1` | 108,233 |
| `mimicgen_hammer_cleanup_d0` | 285,359 | | `mimicgen_stack_three_d0` | 254,810 |
| `mimicgen_hammer_cleanup_d1` | 286,847 | | `mimicgen_stack_three_d1` | 255,096 |
| `mimicgen_kitchen_d0` | 616,751 | | `mimicgen_threading_d0` | 224,508 |
| `mimicgen_kitchen_d1` | 619,273 | | `mimicgen_threading_d1` | 223,115 |
| `mimicgen_mug_cleanup_d0` | 338,136 | | `mimicgen_threading_d2` | 227,084 |
| `mimicgen_mug_cleanup_d1` | 338,034 | | `mimicgen_three_piece_assembly_d0` | 336,695 |
| `mimicgen_nut_assembly_d0` | 358,907 | | `mimicgen_three_piece_assembly_d1` | 334,869 |
| `mimicgen_pick_place_d0` | 677,340 | | `mimicgen_three_piece_assembly_d2` | 335,949 |

The `D0`/`D1`/`D2` suffix is the **environment reset-distribution level**, not a
data-size level. `D0` initializes objects over a region resembling the source
demonstrations; `D1` and `D2` broaden the initial-pose distribution. See the
[MimicGen dataset docs](https://mimicgen.github.io/docs/datasets/mimicgen_corl_2023.html).

### LIBERO — `libero/` (130 tasks, 1,007,618 frames)

The LIBERO-130 lifelong-learning suite, replayed from its recorded states:

| suite | tasks | frames |
|---|---|---|
| `libero_10` | 10 | 138,090 |
| `libero_90` | 90 | 669,043 |
| `libero_goal` | 10 | 63,728 |
| `libero_object` | 10 | 74,507 |
| `libero_spatial` | 10 | 62,250 |

Each task is `libero/<suite>_<scene>_<language-goal>.lance`.

### RoboCasa — `robocasa/` (65 atomic tasks, 1,495,313 frames)

RoboCasa v0.2 atomic kitchen tasks (PandaOmron mobile manipulator, procedurally
generated kitchens), decoded from the released LeRobot videos:

| dataset | frames |
|---|---|
| `robocasa_AdjustToasterOvenTemperature` | 21,328 |
| `robocasa_AdjustWaterTemperature` | 20,953 |
| `robocasa_CheesyBread` | 31,141 |
| `robocasa_CloseBlenderLid` | 36,933 |
| `robocasa_CloseCabinet` | 27,754 |
| `robocasa_CloseDishwasher` | 15,781 |
| `robocasa_CloseDrawer` | 15,670 |
| `robocasa_CloseElectricKettleLid` | 7,530 |
| `robocasa_CloseFridge` | 26,888 |
| `robocasa_CloseFridgeDrawer` | 14,946 |
| `robocasa_CloseMicrowave` | 20,075 |
| `robocasa_CloseOven` | 18,230 |
| `robocasa_CloseStandMixerHead` | 11,593 |
| `robocasa_CloseToasterOvenDoor` | 19,815 |
| `robocasa_CoffeeServeMug` | 16,921 |
| `robocasa_CoffeeSetupMug` | 23,636 |
| `robocasa_LowerHeat` | 31,174 |
| `robocasa_MakeIcedCoffee` | 29,048 |
| `robocasa_NavigateKitchen` | 79,550 |
| `robocasa_OpenBlenderLid` | 20,124 |
| `robocasa_OpenCabinet` | 37,492 |
| `robocasa_OpenDishwasher` | 18,086 |
| `robocasa_OpenDrawer` | 20,488 |
| `robocasa_OpenElectricKettleLid` | 10,928 |
| `robocasa_OpenFridge` | 33,138 |
| `robocasa_OpenFridgeDrawer` | 18,517 |
| `robocasa_OpenMicrowave` | 26,017 |
| `robocasa_OpenOven` | 15,555 |
| `robocasa_OpenStandMixerHead` | 13,411 |
| `robocasa_OpenToasterOvenDoor` | 15,469 |
| `robocasa_PackDessert` | 27,994 |
| `robocasa_PickPlaceCabinetToCounter` | 20,201 |
| `robocasa_PickPlaceCounterToBlender` | 38,892 |
| `robocasa_PickPlaceCounterToCabinet` | 24,225 |
| `robocasa_PickPlaceCounterToDrawer` | 28,225 |
| `robocasa_PickPlaceCounterToMicrowave` | 42,012 |
| `robocasa_PickPlaceCounterToOven` | 32,014 |
| `robocasa_PickPlaceCounterToSink` | 22,410 |
| `robocasa_PickPlaceCounterToStandMixer` | 25,467 |
| `robocasa_PickPlaceCounterToStove` | 24,039 |
| `robocasa_PickPlaceCounterToToasterOven` | 24,313 |
| `robocasa_PickPlaceDrawerToCounter` | 31,819 |
| `robocasa_PickPlaceFridgeDrawerToShelf` | 26,396 |
| `robocasa_PickPlaceFridgeShelfToDrawer` | 27,047 |
| `robocasa_PickPlaceMicrowaveToCounter` | 38,729 |
| `robocasa_PickPlaceSinkToCounter` | 26,397 |
| `robocasa_PickPlaceStoveToCounter` | 23,003 |
| `robocasa_PickPlaceToasterOvenToCounter` | 19,323 |
| `robocasa_PickPlaceToasterToCounter` | 26,907 |
| `robocasa_PreheatOven` | 21,102 |
| `robocasa_SlideDishwasherRack` | 19,052 |
| `robocasa_SlideOvenRack` | 23,958 |
| `robocasa_SlideToasterOvenRack` | 11,496 |
| `robocasa_StartCoffeeMachine` | 13,722 |
| `robocasa_TurnOffMicrowave` | 15,233 |
| `robocasa_TurnOffSinkFaucet` | 12,309 |
| `robocasa_TurnOffStove` | 32,741 |
| `robocasa_TurnOnBlender` | 11,698 |
| `robocasa_TurnOnElectricKettle` | 12,460 |
| `robocasa_TurnOnMicrowave` | 14,010 |
| `robocasa_TurnOnSinkFaucet` | 23,795 |
| `robocasa_TurnOnStove` | 17,910 |
| `robocasa_TurnOnToaster` | 10,042 |
| `robocasa_TurnOnToasterOven` | 17,051 |
| `robocasa_TurnSinkSpout` | 11,130 |

### DexMimicGen — `dexmimicgen/` (9 bimanual tasks, 2,915,177 frames)

[DexMimicGen](https://dexmimicgen.github.io) bimanual dexterous tasks (NVIDIA,
ICRA 2025) across three robot configs — bimanual Panda, Panda + dexterous hands
(`PandaDexRH`/`LH`), and the GR1 humanoid — replayed from recorded states. Native
cameras are agentview + two wrist views, so `sideview` is the injected world camera.
`action` is 14-dim (bimanual Panda) or 24-dim (dexterous / GR1); proprio obs keys
span `robot0`/`robot1` (and GR1 `left`/`right`).

| dataset | frames |
|---|---|
| `dexmimicgen_two_arm_box_cleanup` | 234,398 |
| `dexmimicgen_two_arm_can_sort_random` | 322,073 |
| `dexmimicgen_two_arm_coffee` | 326,707 |
| `dexmimicgen_two_arm_drawer_cleanup` | 298,235 |
| `dexmimicgen_two_arm_lift_tray` | 516,848 |
| `dexmimicgen_two_arm_pouring` | 338,519 |
| `dexmimicgen_two_arm_threading` | 218,858 |
| `dexmimicgen_two_arm_three_piece_assembly` | 239,827 |
| `dexmimicgen_two_arm_transport` | 419,712 |

## Loading

```python
from huggingface_hub import snapshot_download
import lance

local = snapshot_download(
    "MinghaoFu/owam", repo_type="dataset",
    allow_patterns="robomimic/robomimic_lift.lance/*",  # or "libero/*"
)
ds = lance.dataset(f"{local}/robomimic/robomimic_lift.lance")
print(ds.schema.names)               # note: pixels, sideview, robot0_eye_in_hand
print(ds.to_table(limit=2).to_pandas())
```

## Provenance and licensing

The demonstrations were replayed and re-rendered to three 224×224 cameras. They
come from:

- **robomimic** — Mandlekar et al., *What Matters in Learning from Offline Human
  Demonstrations for Robot Manipulation*, CoRL 2021.
- **MimicGen** — Mandlekar et al., *MimicGen: A Data Generation System for
  Scalable Robot Learning using Human Demonstrations*, CoRL 2023.
- **LIBERO** — Liu et al., *LIBERO: Benchmarking Knowledge Transfer for Lifelong
  Robot Learning*, NeurIPS 2023.
- **RoboCasa** — Nasiriany et al., *RoboCasa: Large-Scale Simulation of Everyday
  Tasks for Generalist Robots*, RSS 2024 (v0.2 LeRobot release).
- **DexMimicGen** — Jiang et al., *DexMimicGen: Automated Data Generation for
  Bimanual Dexterous Manipulation via Imitation Learning*, ICRA 2025.

Upstream sources are MIT-licensed; this dataset redistributes derived renders
for research use.