Domain-randomized demonstration dataset for contact-rich peg insertion, collected with full 6-axis Force/Torque sensing. Successor to v0 with 2.3× more episodes, multi-layer domain randomization, and significantly wider force distributions for robust policy training.

Note: For stable downstream references and reproducible training, consider using the semantic-versioned v0.1.1 release.

Part of the ContactBench collection by EXOKERN.

Why v0.1 over v0?

v0 was collected under fixed, ideal conditions: single friction value, perfect alignment, nominal mass. The resulting policies achieve 100% success in simulation but may struggle under real-world variation.

v0.1 trains policies to handle the variation they will encounter in deployment: different surface finishes (friction 0.2–1.2), object mass uncertainty (±30%), gripper placement error (±8 mm, ±5 deg), and controller gain mismatch (±20–30%).

The F/T channel becomes more valuable under DR — our v0 ablation showed 38% average force reduction with F/T; we expect this gap to widen under domain randomization because force feedback is essential for adapting to varied contact conditions.

Metric	v0	v0.1
Episodes	2,221	5,000
Frames	330,929	~750,000
Collection regime	Mostly fixed condition	Domain-randomized
Wrench signal	Yes (6-axis)	Yes (6-axis)
Format	LeRobot v3.0	LeRobot v3.0

Dataset Statistics

Metric	Value
Episodes	5,000
Total frames	~750,000
Avg episode length	149
Control Frequency	20 Hz
Robot	Franka FR3 (7-DOF)
Simulator	NVIDIA Isaac Lab
Task	FORGE Peg Insert (`Isaac-Forge-PegInsert-Direct-v0`)
Format	LeRobot v3.0 (Parquet)

Features

Key	Shape	Description
`observation.state`	(24,)	Collapsed policy state tensor exported by the FORGE task
`observation.wrench`	(6,)	Force/Torque vector `[Fx, Fy, Fz, Mx, My, Mz]` in N / N·m
`action`	(7,)	Action vector used for rollout supervision
`timestamp`	scalar	Per-frame timestamp
`frame_index`	scalar	Frame index within episode
`episode_index`	scalar	Episode index
`index`	scalar	Global frame index
`task_index`	scalar	LeRobot task mapping index

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 Range	Field	Dim	Unit
0–2	`fingertip_pos`	3	m
3–5	`fingertip_pos_rel_fixed`	3	m
6–9	`fingertip_quat`	4	—
10–12	`ee_linvel`	3	m/s
13–15	`ee_angvel`	3	rad/s
16–21	`force_sensor_smooth`	6	N / N·m
22	`force_threshold`	1	N
23	`ema_factor`	1	—

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.

Implementation reference: These fields correspond to OBS_DIM_CFG in factory_env_cfg.py (indices 0–15) extended by the FORGE force-sensing additions (indices 16–23). See Noseworthy et al., 2024 §III for details.

Wrench Specification

The wrench is reported at the end-effector body frame attached to the Franka gripper link (panda_hand). 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).

Component	Index	Unit	Description
Fx	0	N	Force along x-axis
Fy	1	N	Force along y-axis
Fz	2	N	Force along z-axis (insertion axis)
Mx	3	N·m	Torque about x-axis
My	4	N·m	Torque about y-axis
Mz	5	N·m	Torque about z-axis

Domain Randomization Stack

All layers applied cumulatively during data generation (not post-processing):

Layer	Parameter	Range	Application mode
1 - Geometric init	Peg x/y offset	`[-8 mm, +8 mm]`	per episode reset
1 - Geometric init	Peg z offset	`[-3 mm, +5 mm]`	per episode reset
1 - Geometric init	Peg roll/pitch	`±5 deg`	per episode reset
1 - Geometric init	Peg yaw	`±10 deg`	per episode reset
2 - Object physics	Peg mass scale	`[0.7, 1.3]`	reset/startup (runner dependent)
3 - Contact materials	Static friction	`[0.2, 1.2]`	per episode reset
3 - Contact materials	Dynamic friction	`[0.15, 0.9]`	per episode reset
3 - Contact materials	Restitution	`[0.0, 0.3]`	per episode reset
4 - Robot dynamics	Stiffness scale	`[0.8, 1.2]` (log-uniform)	per episode reset
4 - Robot dynamics	Damping scale	`[0.7, 1.3]` (log-uniform)	per episode reset
6/7 - Noise models	Observation/action Gaussian noise	active in v0.1 run config	runtime attachment

Implementation notes:

Final Option-A production run used fixed joint profile nominal (friction/armature ×1.0).
Dedicated joint friction/armature EventTerm exists in code but was disabled for this final production collection.

Not included in v0.1 (deferred to v1): Sensor noise injection, action noise, joint friction/armature randomization, geometry variation.

Force/Torque QC Snapshot

Metric	Value
Episodes with wrench data	`5000 / 5000` (100%)
Episode length min/mean/max	149 / 149.0 / 149
Contact-like episodes (QC proxy)	`4298 / 5000` (86.0%)
Contact-like criterion	`max(‖F_xyz‖) > 2.0 N` per episode
Peak force quantiles (0/50/90/99/100%)	`0.04, 8.25, 11.98, 14.91, 24.26 N`
Mean force quantiles (0/50/90/99/100%)	`0.02, 1.65, 5.69, 7.78, 9.14 N`

The wide force distribution (0.04–24.3 N peak) reflects the DR stack: low-friction + good alignment produces near-zero contact force, while high-friction + misalignment + heavy peg produces up to 24 N. This variation is intentional and critical for training robust policies.

The ~14% non-contact-like episodes are valid successful insertions where low friction and good alignment resulted in minimal contact forces — these are not failures.

Collection Infrastructure

Environments: 1,024–2,048 parallel (NVIDIA Isaac Lab on RTX 4090)
Policy: Scripted insertion policy with DR-aware success filtering
Collection time: ~30 minutes wall-clock (massively parallelized)
QC pipeline: Automated wrench validation, force distribution analysis, episode completeness checks

Quick Start

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("EXOKERN/contactbench-forge-peginsert-v0.1")
print(f"Episodes: {ds.num_episodes}, Frames: {len(ds)}")

frame = ds[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")

Load with pure HuggingFace Datasets

from datasets import load_dataset

ds = load_dataset("EXOKERN/contactbench-forge-peginsert-v0.1", split="train")
print(ds[0].keys())

Intended Use

Designed for imitation learning and diffusion-policy training on contact-rich insertion, including:

Force-aware policy learning (full_ft vs no_ft)
Robustness studies under domain randomization
Sim-to-real transfer preparation and stress-testing

Recommended training config (from v0 baseline):

Architecture: Diffusion Policy (71.3M params)
Seeds: 3 × 2 conditions (full_ft / no_ft) = 6 runs
Epochs: 300, batch size 256, lr 1e-4

Not intended for direct safety-critical deployment without additional validation and real-system calibration.

Evaluation Tool

Use exokern-eval to benchmark trained policies against EXOKERN baselines:

pip install exokern-eval
exokern-eval --policy your_checkpoint.pt --env Isaac-Forge-PegInsert-Direct-v0 --episodes 100

Related Resources

Resource	Link
v0 Dataset	EXOKERN/contactbench-forge-peginsert-v0
v0.1.1 Dataset (production)	EXOKERN/contactbench-forge-peginsert-v0.1.1
Skill v0 (trained model)	EXOKERN/skill-forge-peginsert-v0
Skill v0.1.1 (trained model)	EXOKERN/skill-forge-peginsert-v0.1.1
Eval CLI	github.com/Exokern/exokern_eval
EXOKERN Org	huggingface.co/EXOKERN

Related Work

FORGE — Noseworthy et al., "Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty" (ISRR 2024)
Factory — Narang et al., "Factory: Fast Contact for Robotic Assembly" (RSS 2022)
IndustReal — Tang et al., "IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality" (RSS 2023)
Diffusion Policy — Chi et al., "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion" (RSS 2023)
LeRobot — Cadene et al., "LeRobot: State-of-the-art Machine Learning for Real-World Robotics" (2024)

Citation

@dataset{exokern_contactbench_peginsert_v01_2026,
  title   = {ContactBench Forge PegInsert v0.1: Domain-Randomized Manipulation with Force/Torque},
  author  = {{EXOKERN}},
  year    = {2026},
  url     = {https://huggingface.co/datasets/EXOKERN/contactbench-forge-peginsert-v0.1},
  license = {CC-BY-NC-4.0}
}

License

CC-BY-NC 4.0 — Free for research and non-commercial use. For commercial licensing, contact EXOKERN.

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 · 🤗 huggingface.co/EXOKERN · 🐙 github.com/Exokern

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Papers for EXOKERN/contactbench-forge-peginsert-v0.1

FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty

Paper • 2408.04587 • Published Aug 8, 2024