---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- manipulation
- sim
- pick-and-place
- pi0.5
- libero
- behavior-1k
- omnigibson
- franka
size_categories:
- 1K<n<10K
pretty_name: SENTINEL sim pnp multitask, cuRobo-prior shortcut, n10
---

# sim-pnp-multitask-prior-n10-libero

A 1,694-episode LeRobot v2.1 dataset of 13 single-object **pick-and-place** tasks in OmniGibson / BEHAVIOR-1K, collected automatically using a **cuRobo motion-planning shortcut** seeded from a real teleop grasp prior. Same 3-camera LIBERO schema we use for π0.5 sim SFT.

This is the auto-collected sibling of [`IDEAS-Lab-Northwestern/sentinel-pnp-multitask`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sentinel-pnp-multitask) (human teleop) — same task family, methodologically distinct (scripted phase-A grasp + closed-loop place).

## Quick facts

| | |
|---|---|
| Codebase version | LeRobot v2.1 |
| Robot | Franka Panda (longfinger) |
| Total episodes | 1,694 |
| Total frames | 2,505,357 |
| FPS | 30 |
| Tasks | 13 (single-object pnp, "middle of the table" → "green goal") |
| Cameras | 3 × 256×256 H.264 (`image_left`, `image_right`, `wrist_image`) |
| State | 8D (EEF xyz + axis-angle rxyz + gripper L/R) |
| Actions | 7D EEF-delta (`dpos_xyz`, `drot_xyz`) + gripper |
| Chunks | 2 (chunk-000: eps 0–999, chunk-001: eps 1000–1693) |
| Size on disk | ~7.7 GB |

## Collection methodology

Collected on a **vast.ai dual-RTX 4090** instance over ~18.5 h per GPU (~37 GPU-hours total), driven by the `phase_a_grasp_from_dataset` shortcut in [SENTINEL-Lite](https://github.com/666harrypeng/SENTINEL-Lite):

1. **Phase A — grasp**: an example successful grasp is read out of a small **real-teleop LeRobot prior dataset** for each (task, seed) cell. **cuRobo** plans and executes the approach + close in OmniGibson.
2. **Phase B — place**: closed-loop OSC EEF-delta controller (`action_normalize=False`, assisted grasping) drives the object to the green goal region.
3. Per (task, seed) cell the pipeline keeps running until **10 successful episodes** are recorded (the "n10" in the dataset name). Cells with persistent failures are skipped.
4. The two GPUs ran disjoint (task, seed) shards; their outputs were merged afterwards (episode indices and the global `index` column reindexed, `chunks_size=1000` re-shuffled).

Driver logs and the merge script are preserved alongside the source captures on the collection box; the per-shard targets were 99 (task, seed) units done + 15 skipped per GPU (114 planned).

## Tasks

| `task_index` | Prompt |
|---:|---|
| 0 | pick up the teacup in the middle of the table and place it at the green goal |
| 1 | pick up the mug in the middle of the table and place it at the green goal |
| 2 | pick up the coffee cup in the middle of the table and place it at the green goal |
| 3 | pick up the goblet in the middle of the table and place it at the green goal |
| 4 | pick up the bowl in the middle of the table and place it at the green goal |
| 5 | pick up the cocktail glass in the middle of the table and place it at the green goal |
| 6 | pick up the chalice in the middle of the table and place it at the green goal |
| 7 | pick up the decanter in the middle of the table and place it at the green goal |
| 8 | pick up the saucepan in the middle of the table and place it at the green goal |
| 9 | pick up the water glass in the middle of the table and place it at the green goal |
| 10 | pick up the gravy boat in the middle of the table and place it at the green goal |
| 11 | pick up the beaker in the middle of the table and place it at the green goal |
| 12 | pick up the beer glass in the middle of the table and place it at the green goal |

## File layout (LeRobot v2.1)

```
data/chunk-000/episode_000000.parquet … episode_000999.parquet
data/chunk-001/episode_001000.parquet … episode_001693.parquet
videos/chunk-{000,001}/{image_left,image_right,wrist_image}/episode_NNNNNN.mp4
meta/info.json
meta/tasks.jsonl
meta/episodes.jsonl
meta/episodes_stats.jsonl
```

## Loading

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("IDEAS-Lab-Northwestern/sim-pnp-multitask-prior-n10-libero")
print(len(ds), ds.features)
sample = ds[0]  # frame 0 of episode 0
```

## Schema details

```
state  : float32 [8]  = (eef_x, eef_y, eef_z, axisangle_x, axisangle_y, axisangle_z, gripper_l, gripper_r)
actions: float32 [7]  = (dpos_x, dpos_y, dpos_z, drot_x, drot_y, drot_z, gripper)
image_left   : video 256×256×3 RGB, h264, 30fps
image_right  : video 256×256×3 RGB, h264, 30fps
wrist_image  : video 256×256×3 RGB, h264, 30fps
```

## Intended use

LoRA fine-tuning of vision-language-action policies (π0.5, OpenVLA-style) on diverse single-object pnp prompts where the grasp prior is **scripted by cuRobo MP** rather than learned from a small set of human teleops. Pairs well with the human-teleop counterpart for ablations on data-collection methodology.

## Sister datasets (same org)

- [`sentinel-pnp-multitask`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sentinel-pnp-multitask) — human teleop counterpart
- [`sim-stack-flat-30-libero`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sim-stack-flat-30-libero), [`sim-stack-same-30-libero`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sim-stack-same-30-libero) — same schema, stack family
- [`sim-dusty-transfer-30-libero`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sim-dusty-transfer-30-libero), [`sim-lid-transport-food-30-libero`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/sim-lid-transport-food-30-libero) — same schema, other task families

## License

Apache-2.0. The underlying BEHAVIOR-1K assets are subject to their own [terms of use](https://behavior.stanford.edu/).