---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- bimanual
- teleoperation
- manipulation
- dexterous-hand
- guardrails
pretty_name: GLIDE Data
size_categories:
- 100K<n<1M
configs:
- config_name: lift_plate_mixed
  data_files: lift_plate_mixed/data/*/*.parquet
- config_name: lift_plate_success
  data_files: lift_plate_success/data/*/*.parquet
- config_name: stand_marker_mixed
  data_files: stand_marker_mixed/data/*/*.parquet
- config_name: stand_marker_success
  data_files: stand_marker_success/data/*/*.parquet
- config_name: pour_wine_mixed
  data_files: pour_wine_mixed/data/*/*.parquet
- config_name: pour_wine_success
  data_files: pour_wine_success/data/*/*.parquet
---

<h1 align="center" style="font-size: 2em; font-weight: 600; line-height: 1.25; color: #1f2328; margin: 0.67em 0 16px; padding-bottom: 0.3em; border-bottom: 1px solid #d1d9e0;"><img src="img/icon.png" width="24" alt="GLIDE icon" style="display: inline-block; vertical-align: middle; width: 0.8em; margin: 0; border: none;"> GLIDE:<br/>
Learning Beyond What Humans Can Demonstrate</h1>

<p align="center" style="margin: 0 0 16px;">
    <a href="https://yuchen-song.github.io/" style="color: #0969da; text-decoration: none;"><strong style="color: #0969da;">Yuchen Song</strong></a>
    ,
    <a href="https://adityamittal03.github.io/" style="color: #0969da; text-decoration: none;"><strong style="color: #0969da;">Aditya Mittal</strong></a>
    ,
    <a href="https://unnat.github.io/" style="color: #0969da; text-decoration: none;"><strong style="color: #0969da;">Unnat Jain</strong></a>
</p>

<p align="center" style="margin: 0 0 16px;">
    <img src="img/ucirvine-blue.png" height="20" alt="UC Irvine" style="display: inline-block; height: 20px; width: auto; margin: 0; border: none;">
</p>

<p align="center" style="margin: 0 0 16px;">
    <a href="https://arxiv.org/abs/2609.24996"><img src="https://img.shields.io/badge/arXiv-2609.24996-maroon.svg" alt="Paper"></a>
    <a href="https://guardrail-policy.github.io/"><img src="https://img.shields.io/badge/Website-github.io-green" alt="Website"></a>
    <a href="https://github.com/Yuchen-Song/glide_code"><img src="https://img.shields.io/badge/Code-GitHub-blue.svg" alt="Code"></a>
    <a href="https://creativecommons.org/licenses/by/4.0/"><img src="https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg" alt="License: CC BY 4.0"></a>
</p>

<p align="center" style="margin: 0 0 16px;">
  <img src="img/teaser.png" width="90%" alt="GLIDE overview" style="display: inline-block; width: 90%; margin: 0; border: none;"/>
</p>

**TL;DR:** GLIDE uses closed-loop, self-refined guardrails to make infeasible
robot manipulation tasks demonstrable, learnable, and the trained policies more
reliable to deploy.

This repository hosts the real-robot demonstration datasets used to train the
GLIDE π₀.₅ policies. The robot runtime used to collect the data and evaluate
policies is in [glide_code](https://github.com/Yuchen-Song/glide_code).

## Tasks

- **🍅 Tomato plate transfer** (`lift_plate_*`): use both grippers to grasp a
  plate of tomatoes and carry it from the tabletop to an elevated surface
  without tilting the plate or spilling.
- **🖍️ Marker handover & stand** (`stand_marker_*`): grasp a marker with one
  gripper, transfer it to the other gripper, place it upright on the table, and
  withdraw without knocking it over.
- **🍷 Wine serving** (`pour_wine_*`): use a left parallel-jaw gripper to hold a
  bottle and a right 15-DoF CRAFT hand to hold a glass while pouring wine from
  the bottle into the glass.

## Subsets

Each task has two subsets, each a self-contained LeRobot dataset:

- **`*_mixed`**: every recorded demonstration, successful and failed
  (mixed quality).
- **`*_success`**: only the successful episodes from the matching `*_mixed`
  subset.

| Subset | Robot | Episodes | Frames | FPS | Duration | Size |
| --- | --- | ---: | ---: | ---: | ---: | ---: |
| `lift_plate_mixed` | `yam_dual_arm` | 30 | 13,170 | 30 | 7.3 min | 144 MB |
| `lift_plate_success` | `yam_dual_arm` | 19 | 8,508 | 30 | 4.7 min | 94 MB |
| `stand_marker_mixed` | `yam_dual_arm` | 30 | 12,769 | 30 | 7.1 min | 128 MB |
| `stand_marker_success` | `yam_dual_arm` | 22 | 9,216 | 30 | 5.1 min | 93 MB |
| `pour_wine_mixed` | `yam_dual_arm_craft_hand` | 60 | 162,158 | 45 | 60.1 min | 2.3 GB |
| `pour_wine_success` | `yam_dual_arm_craft_hand` | 30 | 83,240 | 45 | 30.8 min | 1.2 GB |

Language instructions (one per task):

| Task | Instruction |
| --- | --- |
| 🍅 Tomato plate transfer | "Use both arms to pick up the plate and put it onto the box" |
| 🖍️ Marker handover & stand | "Pick up the marker with one gripper, transfer it to the other gripper, and lift it upright on the table." |
| 🍷 Wine serving | "Use left gripper to pick up the wine bottle, right hand to pick up the wine cup, and pour wine from bottle to the cup." |

## Data Format

All subsets use the [LeRobot](https://github.com/huggingface/lerobot) dataset
format, `codebase_version: v2.1`:

```
<subset>/
├── meta/
│   ├── info.json              # schema, fps, feature names
│   ├── tasks.jsonl            # language instruction
│   ├── episodes.jsonl         # per-episode length and task
│   └── episodes_stats.jsonl   # per-episode feature statistics
├── data/chunk-000/episode_XXXXXX.parquet
└── videos/chunk-000/observation.images.{head,left_wrist,right_wrist}/episode_XXXXXX.mp4
```

Every subset records three RGB cameras (`head`, `left_wrist`, `right_wrist`) at
640×480 as H.264 MP4, along with the robot state, the executed action, and the
raw teleoperation signals.

### Two-gripper tasks (`lift_plate_*`, `stand_marker_*`)

Two YAM arms, each with a parallel-jaw gripper, teleoperated with Quest
controllers.

| Feature | Shape | Description |
| --- | --- | --- |
| `observation.state` | 14 | `left_joint_0..5`, `left_gripper`, `right_joint_0..5`, `right_gripper` |
| `action` | 14 | Commanded joints and grippers, same order as the state |
| `teleoperation.matrices.{head,left_controller,right_controller}` | 4×4 | Quest head and controller poses |
| `teleoperation.buttons` | 8 | Trigger, squeeze, A/B buttons for each controller |

### CRAFT-hand task (`pour_wine_*`)

A left YAM arm with a parallel-jaw gripper and a right YAM arm with a 15-DoF
CRAFT hand, teleoperated with Quest hand tracking.

| Feature | Shape | Description |
| --- | --- | --- |
| `observation.state` | 28 | `left_joint_0..5`, `left_gripper`, `right_joint_0..5`, `craft_motor_0..14_raw` |
| `action` | 28 | Commanded joints, gripper, and CRAFT motors, same order as the state |
| `teleoperation.matrices.{head,left_hand,right_hand}` | 4×4 | Quest head and wrist poses |
| `teleoperation.landmark_matrices.{left_hand,right_hand}` | 25×4×4 | Quest hand-landmark poses |
| `teleoperation.hand_states` | 12 | Pinch, squeeze, and tap flags and values per hand |
| `teleoperation.tracking` | 9 | Tracking validity, event counts, and packet ages for head and hands |

All subsets also include the standard LeRobot index columns: `timestamp`,
`frame_index`, `episode_index`, `index`, and `task_index`.

## Usage

Download a single subset:

```bash
hf download yuchensong/glide_data \
  --repo-type dataset \
  --include "stand_marker_success/*" \
  --local-dir ./glide_data
```

Load it with a LeRobot release that reads the v2.1 format, such as the version
pinned by [OpenPI](https://github.com/Physical-Intelligence/openpi):

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset(
    repo_id="yuchensong/glide_data",
    root="./glide_data/stand_marker_success",
)
frame = dataset[0]
print(frame["observation.state"].shape, frame["action"].shape)
```

Or read the tabular data directly:

```python
import pandas as pd

df = pd.read_parquet(
    "./glide_data/stand_marker_success/data/chunk-000/episode_000000.parquet"
)
```

To train π₀.₅ policies, follow OpenPI's instructions for computing
normalization statistics and fine-tuning on a local LeRobot dataset.

## License

This dataset is released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license.

## Citation

```bibtex
@article{song2026glide,
  title={Learning Beyond What Humans Can Demonstrate},
  author={Song, Yuchen and Mittal, Aditya and Jain, Unnat},
  journal={arXiv preprint arXiv:2609.24996},
  year={2026}
}
```