---
license: cc-by-4.0
task_categories:
  - robotics
  - tabular-classification
language:
  - en
pretty_name: Hide-and-Seek Dataset
size_categories:
  - 1M<n<10M
tags:
  - robotics
  - tactile-sensing
  - robot-learning
  - bimanual-manipulation
  - object-recognition
  - weight-classification
  - vision-denied
  - multimodal
  - time-series
  - tactile-point-cloud
  - icra-2026
---
# Hide-and-Seek Dataset

The **Hide-and-Seek (HAS)** dataset is a large-scale multimodal tactile dataset for **tactile-only object recognition and retrieval in vision-denied environments**.

This dataset accompanies the ICRA 2026 paper:

**Tactile Hide and Seek: Bimanual Object Blind Search and Retrieval Via Tactile-Only Feedback**

The dataset was collected on the **Tactile Omnidirectional Mobile Manipulator (TOMM)** using hand-mounted tactile skin sensors, wrist force-torque sensing, and robot proprioception. It supports learning-based tactile perception for object classification, weight classification, and bimanual verification.

Project page: https://tactile-hide-and-seek.github.io/

---

## Dataset Summary

HAS contains **1,120,116 frame-wise tactile examples**, approximately **1.12M frames**, grouped into approximately **4.3k trajectories**.

The dataset covers:

- **33 physical objects**
- **1 negative class**
- **61 object-weight categories**
- **4 weight labels**: `none`, `light`, `medium`, `heavy`
- Multimodal tactile observations from robot skin, force-torque sensing, and end-effector pose

The dataset is stored in **Parquet** format and provides official train / validation / test splits.

---

## Data Splits

| Split | Examples |
|---|---:|
| Train | 777,169 |
| Validation | 221,195 |
| Test | 121,752 |
| **Total** | **1,120,116** |

---

## Features

Each row corresponds to one frame of synchronized tactile interaction data.

| Feature | Shape / Type | Description |
|---|---:|---|
| `timestamp` | `float64` | Frame timestamp |
| `episode_index` | `int64` | Episode / trajectory identifier |
| `obs_point_cloud_skin_contact` | `88 × 6` | Tactile contact point cloud from robot skin |
| `mask_point_cloud_skin_contact` | `88` | Boolean mask indicating valid tactile skin points |
| `obs_pose_left` | `7` | Left end-effector pose `[x, y, z, qx, qy, qz, qw]` |
| `obs_skin_prox_left` | `44` | Left-hand tactile skin proximity readings |
| `obs_skin_force_left` | `44` | Left-hand tactile skin force readings |
| `obs_skin_dist_left` | `44` | Left-hand calibrated distance readings |
| `obs_dami_force_left` | `6` | Aggregated DAMI force representation |
| `obs_dami_prox_left` | `6` | Aggregated DAMI proximity representation |
| `obs_ft_left` | `6` | Wrist force-torque wrench |
| `classification_label` | `int64` | Object-weight classification label |

The tactile point cloud has shape `88 × 6`, where the 88 points correspond to tactile skin cells across both robot hands. Each point contains spatial and sensor-derived channels.

---

## Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("TUM-ICS/Hide-and-Seek")

print(dataset)
print(dataset["train"][0].keys())
````

Example output:

```text
DatasetDict({
    train: Dataset(...)
    validation: Dataset(...)
    test: Dataset(...)
})
```

Access one sample:

```python
sample = dataset["train"][0]

point_cloud = sample["obs_point_cloud_skin_contact"]
mask = sample["mask_point_cloud_skin_contact"]
label = sample["classification_label"]

print(len(point_cloud))  # 88
print(label)
```

---

## Task Description

The dataset supports tactile-only classification from robot interaction data.

Given a window of tactile observations, the learning task is to predict:

1. **Object identity**
2. **Weight category**

The ICRA 2026 paper uses a dual-head multimodal classifier that jointly predicts object class and weight class from synchronized tactile modalities.

---

## Modalities

HAS includes five synchronized sensing streams:

1. **Tactile point cloud**
   Contact geometry and sensor intensities from distributed tactile skin cells.

2. **Raw skin signals**
   Per-cell proximity, force, and distance readings.

3. **Virtual skin wrenches**
   Aggregated proximity- and force-based wrench representations.

4. **Proprioceptive pose**
   End-effector pose from the robot kinematic state.

5. **Wrist force-torque wrench**
   Six-axis force / torque measurements from the wrist sensor.

---

## Dataset Collection

The dataset was collected in a vision-denied tabletop workspace using TOMM, a bimanual robot equipped with hand-mounted tactile skin.

The data collection protocol includes:

* Single-hand pushing interactions for tactile object classification
* Bimanual grasping interactions for verification
* Randomized object placements
* Multiple object-weight variants
* Data collection across different days to reduce overfitting to a single sensor state

The official train / validation / test splits are provided for reproducible evaluation.

---

## Baseline Results

The ICRA 2026 paper reports the following representative results on the HAS dataset and online robot trials:

| Metric                                   | Result |
| ---------------------------------------- | -----: |
| Object classification accuracy           |  91.1% |
| Weight classification accuracy           |  83.1% |
| Strict joint accuracy                    |  79.6% |
| Online object + weight retrieval success |  61.4% |

The bimanual verification stage further corrects up to **17.6%** of single-hand errors in online trials.

---

## Label Mapping

The `classification_label` field encodes 61 object-weight categories as integer labels.

The full mapping is provided in [`label_mapping.yaml`](./meta/label_mapping.yaml).

Each label follows the format:

```text
<object_name>_<weight_class>
```
where the weight class is one of:
```text
light, medium, heavy
```
The special label negative corresponds to non-target / negative examples.

---


## Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{fu2026tactilehideandseek,
  title     = {Tactile Hide and Seek: Bimanual Object Blind Search and Retrieval Via Tactile-Only Feedback},
  author    = {Fu, Xiangyu and Xing, Hao and Armleder, Simon and Shen, Wenlan and Wang, Fengyi and Guadarrama-Olvera, Julio Rogelio and Cheng, Gordon},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year      = {2026}
}
```

---

## Contact

For questions, please contact:

**Xiangyu Fu**
Technical University of Munich
Project page: [https://tactile-hide-and-seek.github.io/](https://tactile-hide-and-seek.github.io/)