Add dataset card with full documentation

README.md

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+---
+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
+  - franka
+  - simulation
+  - benchmark
+language:
+  - en
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - robotics
+---
+
+# EXOKERN ContactBench v0 — Peg Insertion with Force/Torque
+
+<p align="center">
+  <b>The first publicly available insertion dataset with calibrated force/torque annotations.</b>
+</p>
+
+## What is this?
+
+This dataset contains **2,221 simulated peg-in-hole insertion episodes** with full **6-axis force/torque (wrench) data** at every timestep. It is generated in NVIDIA Isaac Lab using a Franka Emika Panda robot arm.
+
+**Why this matters:** Over 95% of existing robotics datasets lack force/torque data. Yet contact-rich manipulation tasks — insertion, assembly, cable routing — fundamentally require haptic feedback for reliable execution. This dataset fills that gap.
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Episodes | 2,221 |
+| Total Frames | 330,929 |
+| Avg Episode Length | 149 steps |
+| FPS | 20 Hz |
+| Format | LeRobot v3.0 (Parquet) |
+| Robot | Franka Emika Panda (7-DOF) |
+| Simulator | NVIDIA Isaac Lab 2.3.x |
+| Task | Peg-in-Hole Insertion (FORGE) |
+| Size | ~75 MB |
+
+## Features
+
+Each frame contains:
+
+| Feature | Shape | Description |
+|---------|-------|-------------|
+| `observation.state` | (24,) | Flat observation tensor (joint pos/vel, ee pose, object pose) |
+| **`observation.wrench`** | **(6,)** | **Force/Torque [Fx,Fy,Fz,Mx,My,Mz] in N/Nm** |
+| `action` | (7,) | Joint position targets |
+| `task` | string | Task description |
+
+> **Note:** Isaac Lab Forge PegInsert uses `collapse_obs_dict` — all observations are compressed into a flat 24-tensor. Force/Torque is extracted separately via `env.unwrapped.force_sensor_smooth`.
+
+## Quick Start
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+dataset = LeRobotDataset("exokern/contactbench-forge-peginsert-v0")
+print(f"Episodes: {dataset.num_episodes}, Frames: {len(dataset)}")
+
+# Access a frame
+frame = dataset[0]
+wrench = frame["observation.wrench"]  # Force/Torque tensor (6,)
+print(f"Force: {wrench[:3]} N, Torque: {wrench[3:]} Nm")
+```
+
+## Data Collection
+
+- **Policy:** rl_games PPO, trained ~200 epochs (reward ~352)
+- **Environment:** `Isaac-Forge-PegInsert-Direct-v0` (NVIDIA Isaac Lab 2.3.x)
+- **Collection:** Single-environment rollout (`num_envs=1`), deterministic policy
+- **Force/Torque Source:** `env.unwrapped.force_sensor_smooth` (6-axis, smoothed)
+- **Episode Horizon:** Fixed 149 steps (no early termination)
+
+## Data Quality
+
+- **Sensor Bandwidth:** 20 Hz control frequency
+- **Force Range:** Typical Fx,Fy,Fz: [-50, 50] N; Mx,My,Mz: [-10, 10] Nm
+- **Noise Model:** Isaac Lab default contact dynamics (PhysX GPU)
+- **Domain Randomization:** Default FORGE PegInsert parameters
+
+## Reproduction
+
+```bash
+# 1. Train policy (~20-30 min on RTX 4090)
+./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py \
+    --task Isaac-Forge-PegInsert-Direct-v0 --headless --num_envs 512
+
+# 2. Collect data (~2 episodes/min with num_envs=1)
+./isaaclab.sh -p /workspace/play_with_logging.py \
+    --task Isaac-Forge-PegInsert-Direct-v0 --headless --num_envs 1 \
+    --checkpoint /path/to/Forge.pth
+
+# 3. Build LeRobot dataset (in conda env, NOT Isaac Sim Python!)
+python build_lerobot_dataset.py
+```
+
+## License
+
+CC-BY-NC 4.0 — Free for research and non-commercial use.
+Commercial licensing available from EXOKERN.
+
+## About EXOKERN
+
+**EXOKERN — The Data Engine for Physical AI**
+
+We produce industrially calibrated force/torque manipulation data for enterprise robotics.
+Contact-rich. Sensor-annotated. Industrially validated.
+
+## Citation
+
+```bibtex
+@dataset{exokern_contactbench_v0_2026,
+  title={EXOKERN ContactBench v0: Peg Insertion with Force/Torque},
+  author={EXOKERN},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/exokern/contactbench-forge-peginsert-v0}
+}
+```