--- 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 The first publicly available insertion dataset with calibrated 6-axis force/torque annotations.
Part of the ContactBench collection by EXOKERN — The Data Engine for Physical AI

Force profile during peg insertion episode

--- ## 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} } ```