README.md

---
language:
  - en
  - zh
license: cc-by-sa-4.0
tags:
  - robotics
  - tactile
  - manipulation
  - embodiment-agnostic
  - multimodal
  - video
  - teleoperation
  - lerobot
  - grasping
  - dexterous-manipulation
  - multi-environment
pretty_name: "Embodiment-Agnostic Tactile Manipulation Dataset"
size_categories:
  - 1K<n<10K

---

![rimbot2](https://cdn-uploads.huggingface.co/production/uploads/6a0c0bb7b236464fd3d3a954/Xw4TtEiMrf-21rGa4B6mc.png)

# 🤖 PalmDex: An Embodiment-Agnostic Tactile Manipulation Dataset

A growing, multimodal robotic manipulation dataset featuring **synchronized dual-camera video, dual-hand tactile sensing, and hand pose tracking** across diverse real-world environments. All demonstrations are collected via human teleoperation — without any specific robot embodiment — and annotated at the action-segment level with rich categorical labels.

> [!NOTE]
> **This is a living dataset.** New environments, tasks, and objects will be added over time. The current release covers **chemistry laboratory** operations. Future releases will expand to additional scenes such as kitchens, workshops, offices, grocery stores, and more. See the [Annotation Schema](#-annotation-schema) for the full list of supported environments and object categories.

## 📋 Dataset Summary

| Property | Value |
|---|---|
| **Design philosophy** | Embodiment-agnostic — human hand demonstrations with tactile & visual sensing, transferable to any robot morphology |
| **Environments** | Multi-scene (currently: chemistry lab; planned: kitchen, workshop, office, store, etc.) |
| **Current tasks** | Mortar & pestle grinding, Crucible tongs transfer |
| **Total annotated segments** | **1,001** (and growing) |
| **Total raw episodes** | ~300+ |
| **Modalities** | Ego-view video, side-view video, dual-hand tactile pressure (256-d × 2), hand pose (72-d) |
| **Video resolution** | 640 × 480 @ 30 FPS |
| **Video codec** | AV1 (raw), H.264/MP4 (annotated segments) |
| **Tactile sensor** | Dual tactile gloves, 256 channels per hand, float32 |
| **Total size** | ~8.6 GB (current release) |
| **Data format** | LeRobot v3.0 (raw) + JSONL annotations + Apache Parquet (tactile) |


---

## 💡  Why Embodiment-Agnostic?

Traditional robot manipulation datasets are tightly coupled to a specific robot platform. This dataset takes a fundamentally different approach:

- **Human hands as the universal demonstrator** — Demonstrations are performed by human operators wearing sensorized tactile gloves, capturing the natural dexterity, force modulation, and multi-finger coordination that is difficult to obtain from any single robot embodiment.
- **Transferable to any morphology** — The rich multi-modal signals (vision + tactile + hand pose) can be retargeted to different robot hands, grippers, or end-effectors via learned or analytic mappings.
- **Scalable across environments** — The portable data collection rig allows rapid deployment in diverse real-world settings without robot-specific setup, enabling the dataset to grow across many environments and tasks.

---

## 🗂️ Available Sub-Datasets

The dataset is organized by recording session. Each sub-dataset is a self-contained package with raw data and cleaned annotations. Sub-datasets are grouped below by environment and task.

### 🧪 Chemistry Lab

#### Task 1: Mortar and Pestle Grinding

| Sub-Dataset | Segments | Action Sequence | Size |
|---|---|---|---|
| `0508_1_mortar_pestle` | 214 | picking up → grinding → placing | 1.5 GB |
| `0508_2_mortar_pestle` | 201 | picking up → grinding → placing | 2.4 GB |
| **Subtotal** | **415** | | **3.9 GB** |

#### Task 2: Crucible Tongs Transfer

| Sub-Dataset | Segments | Action Sequence | Size |
|---|---|---|---|
| `0508_3_crucible_tongs_transfer` | 349 | picking up → clamping & transferring → placing → restoring | 1.5 GB |
| `0508_4_crucible_tongs_transfer` | 237 | picking up → clamping & transferring → placing | 3.2 GB |
| **Subtotal** | **586** | | **4.7 GB** |

### 🔜 Coming Soon

Additional environments and tasks are in preparation, including but not limited to:

| Environment | Example Tasks | Status |
|---|---|---|
| Kitchen | Object sorting, tool use | Planned |
| Workshop | Assembly, tool manipulation | Planned |
| Office | Stationery handling, organizing | Planned |
| Grocery Store | Product picking, shelf stocking | Planned |
| Living Room | Everyday object manipulation | Planned |

> Each new environment will follow the same data format and annotation schema, ensuring full compatibility across the entire dataset.

---

## 🏷️ Annotation Schema

The annotation schema is designed to be **environment-agnostic and extensible**. New environments, objects, and actions can be added without breaking existing data.

Each annotated segment is labeled with the following multi-dimensional categorical attributes:

### Label Categories

| Category | Type | Description |
|---|---|---|
| `environment` | single-select | Scene / location of the task |
| `object` | single-select | Object(s) being manipulated |
| `action` | single-select | Fine-grained action label |
| `grasp_type` | single-select | Grasp taxonomy classification |
| `notes` | free-text | Annotator notes per segment |

### Supported Environments (Expandable)

The schema currently supports the following environments, with new entries added as data collection expands:

```
hardware store · office 1 · office 2 · grocery store 1 · grocery store 2
workshop · kitchen · sports store · bedroom · plant store
restaurant 1 · restaurant 2 · living room · biochemistry lab
wet lab bench · solution preparation station · distillation station
fume hood · sterile workbench · analytical instrument station
cold storage area · waste disposal area · chemistry lab · ...
```

### Supported Grasp Types

```
Small Diameter · Tip Pinch · Prismatic 2 Finger · Prismatic 3 Finger
Index Finger Extension · Medium Wrap · Palmar · Fixed Hook
Light Tool · Precision Sphere · ...
```

### Current Action Labels by Task

**Mortar & Pestle Grinding:**
```
picking up  →  grinding  →  placing
```

**Crucible Tongs Transfer:**
```
picking up  →  clamping and transferring  →  placing  [→ restoring object position]
```

### Action Distribution (Current Release)

| Action | 0508_1 | 0508_2 | 0508_3 | 0508_4 | **Total** |
|---|---|---|---|---|---|
| picking up | 72 | 67 | 116 | 79 | **334** |
| grinding | 72 | 67 | — | — | **139** |
| placing | 70 | 67 | 116 | 79 | **332** |
| clamping and transferring | — | — | 116 | 79 | **195** |
| restoring object position | — | — | 1 | — | **1** |

---

## 📁 Directory Structure

Each sub-dataset follows a consistent structure:

```
<dataset_root>/
├── README.md                              ← This file (top-level)
├── <session_id>_<task_name>/              ← Sub-dataset package
│   ├── README.md                          ← Sub-dataset description
│   ├── dataset_metadata.json              ← Machine-readable metadata
│   ├── checksums.sha256                   ← Package-level integrity checksums
│   ├── raw/
│   │   └── lerobot/<raw_id>/             ← Original LeRobot v3.0 dataset
│   │       ├── data/                      ←   Parquet data files (tactile, state, etc.)
│   │       ├── videos/                    ←   Full-episode AV1 video streams
│   │       ├── images/                    ←   (if applicable)
│   │       ├── meta/                      ←   info.json, stats.json, tasks.parquet, ...
│   │       └── sync_diagnostics/          ←   Timing / synchronization logs
│   └── annotated/
│       ├── annotations.jsonl              ← All segment annotations (1 JSON per line)
│       ├── schema.json                    ← Annotation schema definition
│       ├── manifest.json                  ← File manifest for all segments
│       ├── validation_report.json         ← Automated QA report
│       ├── release_report.json            ← Release summary
│       ├── checksums.sha256               ← Annotated-subset checksums
│       └── segments/
│           └── train/
│               └── rbt_epXXXXXX_sXXXXXX/ ← Per-segment directory
│                   ├── ego.mp4            ← Ego-view video clip
│                   ├── side.mp4           ← Side-view video clip
│                   ├── data.parquet       ← Tactile + state data
│                   └── meta.json          ← Segment metadata & labels
├── ...                                    ← More sub-datasets
```

---

## 🎯 Modalities in Detail

### 1. Visual — Dual-Camera Video

Each episode is recorded from two synchronized camera viewpoints:

| Camera | Key | Resolution | FPS | Codec |
|---|---|---|---|---|
| **Ego-view** | `observation.images.ego` | 640 × 480 | 30 | AV1 (raw) / H.264 (annotated) |
| **Side-view** | `observation.images.side` | 640 × 480 | 30 | AV1 (raw) / H.264 (annotated) |

- **Raw episodes**: Full uncut videos stored under `raw/lerobot/*/videos/`.
- **Annotated segments**: Trimmed video clips per action segment stored as `ego.mp4` and `side.mp4`.

### 2. Tactile — Dual-Hand Pressure Arrays

High-resolution tactile data from sensorized gloves, recorded at 30 Hz:

| Channel | Key | Shape | Dtype | Description |
|---|---|---|---|---|
| Left hand | `observation.tactile_left` | (256,) | float32 | Raw pressure values from left-hand tactile glove |
| Right hand | `observation.tactile_right` | (256,) | float32 | Raw pressure values from right-hand tactile glove |

- Tactile routing protocol: Header (4 bytes) + Sequence (1 byte) + Type (1 byte) + Data
- Type `0x01` → Left hand, Type `0x02` → Right hand
- Per-segment tactile data is stored in `data.parquet` files

### 3. Hand Pose — Teleoperation State

Full articulated hand pose captured via the UDexReal hand-tracking system:

| Key | Shape | Dtype | Description |
|---|---|---|---|
| `observation.udexreal` | (72,) | float32 | Dual-hand bone positions, rotations, scales, and finger joint parameters |

The 72-dimensional vector encodes:
- Head bone: Location (3D), Parent, Rotation (3D), Scale (3D) — 10 dims
- Left hand: Calibration status + 24 joint parameters + joystick/button states — 31 dims
- Right hand: Calibration status + 24 joint parameters + joystick/button states — 31 dims

---

## 🔖 Segment ID Convention

All segment IDs follow the format:

```
rbt_ep{EPISODE:06d}_s{SEGMENT:06d}
```

- `ep` = Episode index (from the original LeRobot dataset)
- `s` = Globally unique segment index within the sub-dataset

Example: `rbt_ep000002_s000001` = Episode 2, Segment 1

---

## 📊 JSONL Annotation Format

Each line in `annotations.jsonl` is a self-contained JSON object:

```json
{
  "segment_id": "rbt_ep000002_s000001",
  "episode_idx": 2,
  "start_frame": 6,
  "end_frame": 94,
  "labels": {
    "environment": "chemistry lab",
    "object": "mortar and pestle",
    "action": "picking up",
    "grasp_type": "Small Diameter"
  },
  "outcome": {
    "success_frame": null,
    "final_success": null,
    "events": []
  },
  "notes": "研磨物体",
  "workflow_status": "approved"
}
```

### Per-Segment `meta.json` Format

```json
{
  "segment_id": "rbt_ep000002_s000001",
  "source": {
    "dataset_id": "0508",
    "episode_idx": 2,
    "start_frame": 6,
    "end_frame": 94,
    "start_time_sec": 0.2,
    "end_time_sec": 3.133,
    "fps": 30.0
  },
  "labels": {
    "environment": "chemistry lab",
    "object": "mortar and pestle",
    "action": "picking up",
    "grasp_type": "Small Diameter"
  },
  "outcome": { ... },
  "notes": "研磨物体"
}
```

---

## 🚀 Quick Start

### Loading Annotations

```python
import json

annotations = []
with open("0508_1_mortar_pestle/annotated/annotations.jsonl") as f:
    for line in f:
        annotations.append(json.loads(line))

# Filter by action
grinding_segments = [a for a in annotations if a["labels"]["action"] == "grinding"]
print(f"Grinding segments: {len(grinding_segments)}")
```

### Loading Tactile Data

```python
import pandas as pd

# Load per-segment tactile data
df = pd.read_parquet(
    "0508_1_mortar_pestle/annotated/segments/train/rbt_ep000002_s000001/data.parquet"
)
print(df.columns.tolist())
print(df.shape)
```

### Loading Segment Video

```python
import cv2

cap = cv2.VideoCapture(
    "0508_1_mortar_pestle/annotated/segments/train/rbt_ep000002_s000001/ego.mp4"
)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(f"FPS: {fps}, Frames: {frame_count}")
cap.release()
```

### Using with LeRobot (Raw Data)

The `raw/lerobot/` directories are compatible with the [LeRobot](https://github.com/huggingface/lerobot) framework (codebase v3.0):

```python
# Raw dataset can be loaded with LeRobot's dataset loader
# Refer to https://github.com/huggingface/lerobot for details
```

---

## ✅ Data Quality & Validation

- All annotated segments have passed automated validation (0 P0 critical errors)
- Validation issues are limited to P1 informational notes (`P1_LEGACY_APPROVED_NEEDS_REVIEW` — indicating segments migrated from a legacy annotation workflow)
- SHA-256 checksums are provided at both the package level (`checksums.sha256`) and the annotated subset level (`annotated/checksums.sha256`)

### Validation Summary (Current Release)

| Sub-Dataset | P0 (Critical) | P1 (Info) | P2 (Warning) |
|---|---|---|---|
| 0508_1_mortar_pestle | 0 | 214 | 0 |
| 0508_2_mortar_pestle | 0 | 201 | 0 |
| 0508_3_crucible_tongs_transfer | 0 | 345 | 0 |
| 0508_4_crucible_tongs_transfer | 0 | 237 | 0 |

### Integrity Verification

```bash
# Verify full package integrity
cd 0508_1_mortar_pestle
sha256sum -c checksums.sha256

# Verify annotated subset only
cd annotated
sha256sum -c checksums.sha256
```

---

## 📝 Processing Notes

### Label Normalization
- In sub-datasets `0508_1` and `0508_4`, the original `moving` and `picking up` stages were merged into a single `picking up` label to ensure consistency across the dataset.
- In `0508_4`, the original `adjusting` stage was normalized to `clamping and transferring`.

### Scope of 0508_4
The original `0508_4` LeRobot recording contains more than one task. Episodes 001–096 correspond to the crucible-tongs transfer task, while episodes starting from 097 correspond to stirring a beaker solution with a glass rod. Only the crucible-tongs transfer subset (79 cleaned episodes) is included in the annotated release. Episodes 080 and 095 were excluded during quality control.

---

## 🏗️ Data Collection Setup

| Component | Specification |
|---|---|
| **Teleoperation system** | UDexReal hand-tracking gloves with integrated tactile sensing |
| **Cameras** | Dual RGB cameras (ego-view + side-view), 640×480, 30 FPS |
| **Tactile sensors** | Dual tactile gloves, 256 pressure channels per hand |
| **Recording framework** | LeRobot v3.0 |
| **Annotation tool** | Custom JSONL-based annotation pipeline with schema validation |
| **Data synchronization** | Hardware-triggered, verified via `sync_diagnostics/` |

The entire collection rig is portable and can be deployed across different environments without any robot-specific hardware, enabling rapid scaling to new scenes and tasks.

---


---

## 📖 Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{tactile_manipulation_2026,
  title     = {Embodiment-Agnostic Tactile Manipulation Dataset},
  author    = {Rimbot},
  year      = {2026},
  publisher = {Hugging Face},
  note      = {A growing multimodal dataset with synchronized dual-camera
               video, dual-hand tactile sensing, and hand pose tracking
               for dexterous manipulation across diverse real-world environments}
}
```

---

## 📬 Contact

For questions, issues, or collaboration inquiries, please open an issue on this Hugging Face dataset repository.

---

## 📅 Changelog

| Date | Update |
|---|---|
| 2026-05-23 | Initial release — Chemistry Lab: mortar & pestle grinding (×2) + crucible tongs transfer (×2) |