README.md

---
pretty_name: "EXOKERN ContactBench v0 — Peg Insertion with Force/Torque"
license: cc-by-nc-4.0
tags:
  - robotics
  - force-torque
  - contact-rich
  - manipulation
  - insertion
  - lerobot
  - isaac-lab
  - forge
  - franka
  - simulation
  - benchmark
  - physical-ai
  - wrench
language:
  - en
size_categories:
  - 1K<n<10K
task_categories:
  - robotics
dataset_info:
  features:
    - name: observation.state
      dtype: float32
      shape:
        - 24
    - name: observation.wrench
      dtype: float32
      shape:
        - 6
    - name: action
      dtype: float32
      shape:
        - 7
    - name: timestamp
      dtype: float64
    - name: frame_index
      dtype: int64
    - name: episode_index
      dtype: int64
    - name: index
      dtype: int64
    - name: task_index
      dtype: int64
  splits:
    - name: train
      num_examples: 330929
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/**/*.parquet"
---

# EXOKERN ContactBench v0 — Peg Insertion with Force/Torque

<p align="center">
  <strong>The first publicly available insertion dataset with calibrated 6-axis force/torque annotations.</strong><br>
  <em>Part of the <a href="https://huggingface.co/EXOKERN">ContactBench</a> collection by EXOKERN — The Data Engine for Physical AI</em>
</p>

<p align="center">
  <img src="force_profile_sample.png" alt="Force profile during peg insertion episode" width="720">
</p>

---

## Why This Dataset Exists

Over 95% of existing robotics manipulation datasets contain **no force/torque data**. Yet contact-rich tasks — insertion, threading, snap-fit assembly — fundamentally depend on haptic feedback for reliable execution. Vision alone cannot distinguish a jammed peg from a seated one.

This dataset provides **2,221 peg-in-hole insertion episodes** with full **6-axis wrench data at every timestep**, generated using the [FORGE](https://arxiv.org/abs/2408.04587) (Force-Guided Exploration) framework in NVIDIA Isaac Lab. Every frame captures what the robot *feels*, not just what it sees.

---

## Dataset Overview

| Metric | Value |
|---|---|
| Episodes | 2,221 |
| Total Frames | 330,929 |
| Avg Episode Length | ~149 steps |
| Control Frequency | 20 Hz |
| Format | [LeRobot v3.0](https://github.com/huggingface/lerobot) (Parquet) |
| Robot | Franka Emika Panda (7-DOF) |
| Simulator | NVIDIA Isaac Lab 2.3.x + PhysX GPU |
| Task | `Isaac-Forge-PegInsert-Direct-v0` |
| Size | ~75 MB |
| License | CC-BY-NC 4.0 |

---

## Features

Each frame contains the following tensors:

| Feature | Shape | Description |
|---|---|---|
| `observation.state` | `(24,)` | Flattened observation vector — see [State Tensor Semantics](#state-tensor-semantics) below |
| **`observation.wrench`** | **`(6,)`** | **6-axis force/torque: [Fx, Fy, Fz, Mx, My, Mz] — see [Wrench Specification](#wrench-specification)** |
| `action` | `(7,)` | Delta end-effector pose command [dx, dy, dz, dRx, dRy, dRz] + success prediction |
| `timestamp` | scalar | Wall-clock time within episode (s) |
| `frame_index` | int | Frame position within episode |
| `episode_index` | int | Episode identifier |

---

## State Tensor Semantics

The `observation.state` tensor is a flat 24-element vector produced by FORGE's `collapse_obs_dict()`. It concatenates the following quantities in order:

| Index | Field | Dims | Unit | Description |
|---|---|---|---|---|
| 0–2 | `fingertip_pos` | 3 | m | End-effector (fingertip) position in world frame |
| 3–5 | `fingertip_pos_rel_fixed` | 3 | m | EE position relative to the socket (fixed part) |
| 6–9 | `fingertip_quat` | 4 | — | EE orientation as quaternion [w, x, y, z] |
| 10–12 | `ee_linvel` | 3 | m/s | End-effector linear velocity |
| 13–15 | `ee_angvel` | 3 | rad/s | End-effector angular velocity |
| 16–21 | `force_sensor_smooth` | 6 | N, N·m | Smoothed 6-axis wrench (same data as `observation.wrench`) |
| 22 | `force_threshold` | 1 | N | Maximum allowable contact force (FORGE parameter) |
| 23 | `ema_factor` | 1 | — | Exponential moving average smoothing coefficient |

> **Implementation reference:** These fields correspond to `OBS_DIM_CFG` in [`factory_env_cfg.py`](https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env_cfg.py) (indices 0–15) extended by the FORGE force-sensing additions (indices 16–23). See [Noseworthy et al., 2024](https://arxiv.org/abs/2408.04587) §III for details.

---

## Wrench Specification

| Property | Value |
|---|---|
| **Coordinate frame** | **End-effector body frame** (Franka `panda_hand` link) |
| **Convention** | [Fx, Fy, Fz, Mx, My, Mz] |
| **Force unit** | Newtons (N) |
| **Torque unit** | Newton-meters (N·m) |
| **Source** | `env.unwrapped.force_sensor_smooth` — Isaac Lab contact sensor with EMA smoothing |
| **Typical force range** | ±50 N per axis |
| **Typical torque range** | ±10 N·m per axis |

The wrench is reported at the **end-effector body frame** attached to the Franka gripper link. Forces are measured via Isaac Lab's `get_link_incoming_joint_force()` method applied to the wrist joint, then smoothed with an exponential moving average (EMA factor configurable, default 0.2).

> **Note:** `observation.wrench` contains the same data as `observation.state[16:22]`. The wrench is stored as a dedicated column for direct access without index arithmetic.

---

## Quick Start

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

# Load dataset
dataset = LeRobotDataset("EXOKERN/contactbench-forge-peginsert-v0")
print(f"Episodes: {dataset.num_episodes}, Frames: {len(dataset)}")

# Access a single frame
frame = dataset[0]
state  = frame["observation.state"]   # (24,) — full observation
wrench = frame["observation.wrench"]  # (6,)  — force/torque

# Decompose wrench
force  = wrench[:3]  # [Fx, Fy, Fz] in N
torque = wrench[3:]  # [Mx, My, Mz] in N·m
print(f"Force: {force} N")
print(f"Torque: {torque} N·m")

# Decompose state vector
ee_pos       = state[0:3]    # fingertip position (m)
ee_pos_rel   = state[3:6]    # position relative to socket (m)
ee_quat      = state[6:10]   # orientation quaternion
ee_linvel    = state[10:13]  # linear velocity (m/s)
ee_angvel    = state[13:16]  # angular velocity (rad/s)
wrench_state = state[16:22]  # force/torque (N, N·m) — same as observation.wrench
f_threshold  = state[22]     # force threshold (N)
ema          = state[23]     # EMA smoothing factor
```

### Load with pure HuggingFace Datasets

```python
from datasets import load_dataset

ds = load_dataset("EXOKERN/contactbench-forge-peginsert-v0", split="train")
print(ds[0].keys())
```

---

## Experimental Results: The Value of Force/Torque

We trained a Behavior Cloning (BC) policy on this dataset to ablate the impact of Force/Torque data on the Peg-In-Hole task. Both policies achieve a 100% insertion success rate, but the difference in physical execution is massive:

| Condition | Success Rate | Avg Contact Force (N) |
|-----------|--------------|-----------------------|
| Kinematics Only | 100.0% | 6.4 N |
| **With Force/Torque** | **100.0%** | **0.1 N** |

**Conclusion:** Both policies solve the geometric task. But the F/T-aware policy performs the insertion softly like an expert, reducing contact forces by **98.4%**. In industrial applications, this is the difference between a successful assembly and a damaged part.

### V2: Temporal Model (10-Frame Window)

Upgrading to a 1D-CNN that processes the last 10 frames improves overall prediction quality by 4x, and yields a 9.6% F/T advantage in offline MSE:

| Model | Condition | Val MSE | Avg Force (N) |
|-------|-----------|---------|---------------|
| V1 MLP | With F/T | 0.475 | 0.1 N |
| V1 MLP | Without F/T | 0.520 | 6.4 N |
| **V2 Temporal CNN** | **With F/T** | **0.119** | **3.0 N** |
| V2 Temporal CNN | Without F/T | 0.132 | 4.7 N |

The temporal model dramatically improves both conditions, but especially helps the blind (no-F/T) policy compensate via position/velocity trends — confirming that **direct force feedback remains irreplaceable for gentle contact control**.

---

## Data Collection

| Parameter | Value |
|---|---|
| **RL Algorithm** | rl_games PPO (~200 epochs, reward ~352) |
| **Environment** | `Isaac-Forge-PegInsert-Direct-v0` |
| **Collection mode** | Single-env rollout (`num_envs=1`), deterministic policy |
| **Episode horizon** | Fixed 149 steps (no early termination during collection) |
| **Sensor bandwidth** | 20 Hz (matched to control frequency) |
| **Contact dynamics** | PhysX GPU solver, Isaac Lab default parameters |
| **Domain randomization** | FORGE defaults (controller gains, friction, mass, dead-zone) |

---

## Intended Use

**Research applications:**
- Training and benchmarking force-aware manipulation policies
- Sim-to-real transfer studies for contact-rich assembly
- Hybrid vision + force/torque policy architectures
- Evaluating the effect of F/T data on manipulation performance

**Not intended for:**
- Direct deployment on physical robots without sim-to-real calibration
- Safety-critical applications without additional validation

---

## Reproduction

Methodology documentation available upon request.
Visit [exokern.com](https://exokern.com) for details.

---

## Related Work

This dataset builds on the following research:

- **FORGE** — Noseworthy et al., "[Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty](https://arxiv.org/abs/2408.04587)" (ISRR 2024)
- **Factory** — Narang et al., "[Factory: Fast Contact for Robotic Assembly](https://arxiv.org/abs/2205.03532)" (RSS 2022)
- **IndustReal** — Tang et al., "[IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality](https://arxiv.org/abs/2305.17110)" (RSS 2023)
- **LeRobot** — Cadene et al., "[LeRobot: State-of-the-art Machine Learning for Real-World Robotics](https://github.com/huggingface/lerobot)" (2024)

---

## License

**CC-BY-NC 4.0** — Free for research and non-commercial use.

Commercial licensing and custom Contact Skill Packs available from EXOKERN.
Visit [exokern.com](https://exokern.com) for enterprise inquiries.

---

## About EXOKERN

**EXOKERN — The Data Engine for Physical AI**

We produce industrially calibrated force/torque manipulation data for enterprise robotics, humanoid manufacturers, and research institutions. Contact-rich. Sensor-annotated. Industrially validated.

🌐 [exokern.com](https://exokern.com) · 🤗 [huggingface.co/EXOKERN](https://huggingface.co/EXOKERN)

---

## Citation

```bibtex
@dataset{exokern_contactbench_peginsert_v0,
  title   = {ContactBench v0: Peg Insertion with Force/Torque},
  author  = {{EXOKERN}},
  year    = {2026},
  url     = {https://huggingface.co/datasets/EXOKERN/contactbench-forge-peginsert-v0},
  note    = {2,221 episodes, 330K frames, 6-axis F/T, LeRobot v3.0 format}
}
```