MinghaoFu/owam

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

239 manipulation datasets in the Lance format, across seven collections:

directory	datasets	contents
libero/	130	LIBERO-130 tasks (90 + 10 + object + spatial + goal)
robocasa/	65	RoboCasa atomic kitchen tasks
mimicgen/	26	MimicGen task/level variants
dexmimicgen/	9	DexMimicGen bimanual dexterous tasks
robomimic/	5	robomimic 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 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.

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 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

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.