eval_index int64 0 19 | scene stringlengths 16 16 | suite stringclasses 1
value | seed int64 0 19 | instruction stringclasses 1
value | target dict | receptacle dict | files dict | sampler dict |
|---|---|---|---|---|---|---|---|---|
0 | eval_big100_s000 | base | 0 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
-0.0126,
0.0015
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s000/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
1 | eval_big100_s001 | base | 1 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
-0.0372,
-0.0766
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s001/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
2 | eval_big100_s002 | base | 2 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
-0.0347,
0.2301
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s002/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
3 | eval_big100_s003 | base | 3 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0527,
-0.0613
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s003/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
4 | eval_big100_s004 | base | 4 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1736,
-0.054
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s004/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
5 | eval_big100_s005 | base | 5 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1066,
0.2441
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s005/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
6 | eval_big100_s006 | base | 6 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0562,
-0.1124
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s006/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
7 | eval_big100_s007 | base | 7 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0542,
0.3111
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s007/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
8 | eval_big100_s008 | base | 8 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0889,
-0.0691
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s008/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
9 | eval_big100_s009 | base | 9 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
-0.0587,
0.2816
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s009/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
10 | eval_big100_s010 | base | 10 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1253,
0.191
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s010/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
11 | eval_big100_s011 | base | 11 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0839,
0.3106
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s011/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
12 | eval_big100_s012 | base | 12 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1641,
-0.0142
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s012/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
13 | eval_big100_s013 | base | 13 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1463,
0.1151
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s013/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
14 | eval_big100_s014 | base | 14 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1545,
-0.1274
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s014/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
15 | eval_big100_s015 | base | 15 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
-0.0622,
0.0903
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s015/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
16 | eval_big100_s016 | base | 16 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0163,
0.2885
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s016/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
17 | eval_big100_s017 | base | 17 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0248,
-0.3006
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s017/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
18 | eval_big100_s018 | base | 18 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.1206,
0.2904
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s018/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
19 | eval_big100_s019 | base | 19 | Pick up the coke can and place it in the terracotta dish. | {
"label": "Coke can",
"uid": "013b0fff25ab49c08ba1195ca7d7df46",
"xy_m": [
0.0187,
0.2552
]
} | {
"label": "Terracotta dish",
"xy_m": [
-0.2,
-0.7
]
} | {
"layout": "eval/scenes/eval_big100_s019/layout.yaml"
} | {
"kind": "floor_uniform",
"floor_bounds_m": [
-0.065,
0.185,
-0.375,
0.375
],
"wall_margin_m": 0.1,
"note": "no filtering by IK count or expert success"
} |
Franka PnP Big-100 — Base
100 shelf pick-and-place demonstrations with no clutter: one coke can on the floor of a large shelf, a terracotta dish on the desk to the left.
Collected in Isaac Lab (Isaac Lab Arena) with a Franka Panda and a cuRobo-planned scripted expert. One successful episode per scene; the can position is sampled uniformly over the shelf floor (see Sampling). This is one of a pair of datasets: franka_pnp_big100_base (no clutter) and franka_pnp_big100_distract (paired, non-blocking clutter).
| Episodes / frames | 100 / 35903 |
| Dataset fps | 10 (recorded at 50 Hz, every 5th step) |
| Episode length | mean 35.9 s (min 19.4 s, max 48.9 s) |
| Cameras | 3 × 320×320 RGB, h264 |
| Robot | Franka Panda (Franka Hand), base at (−0.45, 0, 0.72) m |
| Format | LeRobot v2.1 (meta/info.json), GR00T meta/modality.json |
| Task | "Pick up the coke can and place it in the terracotta dish." |
Scene
- Desk 0.76 × 1.70 m, top at z = 0.76 m. Big shelf on the desk: interior 0.45 (depth) × 0.95 (width) × 0.60 m (height), mouth facing the robot, walls 3 cm.
- Target: Coke can (Objaverse
013b0fff…, Ø 6.6 cm). Receptacle: terracotta dish at (−0.20, −0.70) m on the desk, left of the shelf, fixed. - Success = the can rests inside the dish at the end of the episode.
Sampling
- Can position: uniform over the shelf floor, keeping 10 cm (the gripper half-width) from the side/back walls and from the mouth plane: x ∈ [−0.065, 0.185], y ∈ [−0.375, 0.375] m (robot frame; +x into the shelf). 100 accepted positions span x −0.064…0.184, y −0.358…0.361.
- Acceptance: a position is kept if the expert succeeds within its first 40 planned grasp candidates; 28 sampled positions were discarded (no success in the batch) and 4 more were dropped because no distractor pairing succeeded. This biases the set toward positions the scripted expert can solve; the left/right shelf edges are under-represented (front/back × left/center/right: {"back-center": 21, "back-left": 17, "back-right": 11, "front-center": 16, "front-left": 23, "front-right": 12}).
Expert
- Grasps: 1000 MolmoSpaces-style annotated grasps on the can; grasp + 1 cm pre-grasp IK-checked with cuRobo (shelf/desk collision, joint-limit margin 0.12 rad); ranked by MolmoSpaces cost + axis-offset penalty, near-frontal grasps (|approach yaw| ≤ 30°) first.
- Plan: cuRobo trajectory to the pre-grasp, 1 cm straight advance, 3 cm lift, 1 cm straight extract, retrieval to above the dish, release.
- Execution: absolute joint-position targets at 50 Hz, linear interpolation along the planned dense path at 0.6 rad/s (0.3 rad/s while carrying), settle to 0.005 rad at segment ends, Gaussian target noise σ = 0.002 rad (0.11°) per step. Ramp targets anchored on the previous waypoint (fix of 2026-09-08; earlier data had a saw-tooth wrist jitter).
- Per-attempt success rate of the expert ≈ 10–16 %; episodes here are the first success per scene (mean 4.5 failed attempts before it).
Features
| key | shape | meaning |
|---|---|---|
observation.state |
8 | panda_joint1–7 (rad) + panda_finger_joint1 (m, 0.04 open) |
action |
8 | absolute joint targets panda_joint1–7 (rad) + gripper command (+1 open / −1 close) |
observation.images.exterior_image_1_left |
320×320×3 | left tripod camera at (−0.55, −0.45, 1.30) m looking at (0.05, −0.22, 0.81) m |
observation.images.exterior_image_2_left |
320×320×3 | right tripod camera (y-mirror of the left one) |
observation.images.wrist_image_left |
320×320×3 | wrist camera on the hand's −x face, looking along the fingers |
Cameras use the ZED Mini lens (f = 2.8 mm, 5.376 mm aperture) at a square 320×320 sensor. Camera keys follow DROID naming.
Extra files
meta/scenes.jsonl— one row per episode: scene name, seed, can xy, dish xy, executed grasp (candidate rank, annotation index, approach yaw), failed attempts, raw frame count, paths to the scene files.meta/collection.json— expert and camera settings.scenes/<scene>/layout.yaml— full scene description (desk, shelf boxes, target, receptacle, obstacles by asset uid);scenes/<scene>/plan_cNNN.yaml— the executed grasp chain (joint paths per segment).assets/<uid>/— visual OBJ/MTL/PNG, collision OBJ and rigid USD for the can, the dish and every distractor referenced by the layouts (Objaverse-derived; per-object authors and licenses inATTRIBUTION.md: 99 × CC BY, 1 × CC BY-SA, 2 × CC BY-NC).eval/— 20 held-out evaluation layouts sampled the same way (no filtering by expert success). Eachlayout.yamlcarriesinit_arm_q, the episode-start arm pose every demo was recorded from ([0, -1.3, 0, -2.5, 0, 1.5, 0.8]rad, gripper open 0.04 m); start evaluation episodes from it so the first observations match the training distribution (also inmeta/collection.jsonasepisode_start_arm_q).
Pairing
Episode i of franka_pnp_big100_base and episode i of franka_pnp_big100_distract are the same scene (same can position); see PAIRING.md.
Known limitations
- Scripted expert with ≈ 10 % per-attempt success: every episode is a success, but the set of positions is biased toward what the expert could solve (see Sampling).
- Distractors never sit close to the target (≥ 35 cm) by construction.
- Simulation only (PhysX); the can is a rigid convex-decomposed mesh.
- Receptacle is always on the left.
- Downloads last month
- 222