Versioned production release of the EXOKERN DR insertion corpus for LeRobot training. This dataset contains contact-rich peg insertion demonstrations with full 6-axis wrench signals and multi-layer domain randomization.

Part of the ContactBench collection by EXOKERN — The Data Engine for Physical AI.

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 5,000 domain-randomized peg-in-hole insertion episodes with full 6-axis wrench data at every timestep, generated using the FORGE (Force-Guided Exploration) framework in NVIDIA Isaac Lab. Every frame captures what the robot feels, not just what it sees — under varying physical conditions that simulate real-world uncertainty.

Release Positioning

v0: fixed-condition baseline dataset (2,221 episodes) — link
v0.1: first DR release (5,000 episodes) — link
v0.1.1 ← this release: semantic versioned production publish for stable downstream references and reproducible training/evaluation

Dataset Statistics

Metric	v0.1.1	v0
Episodes	5,000	2,221
Total Frames	745,000	330,929
Avg Episode Length	149	149
Control Frequency	20 Hz	20 Hz
Format	LeRobot v3.0 (Parquet)	LeRobot v3.0 (Parquet)
Robot	Franka FR3 (7-DOF)	Franka FR3 (7-DOF)
Simulator	NVIDIA Isaac Lab	NVIDIA Isaac Lab
Domain Randomization	Active multi-layer stack	Mostly fixed conditions

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

Randomization is applied 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.

Force/Torque QC Snapshot

Metric	Value
Episodes with wrench data	`5000 / 5000` (100%)
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%)	`0.0388, 8.2533, 11.9820, 14.9128, 24.2634 N`
Mean force quantiles (0/50/90/99/100%)	`0.0189, 1.6533, 5.6933, 7.7840, 9.1356 N`

This contact-like metric is a quality-control proxy, not a ground-truth success label.

Quick Start

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("EXOKERN/contactbench-forge-peginsert-v0.1.1")
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")

# 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_st   = 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

from datasets import load_dataset

ds = load_dataset("EXOKERN/contactbench-forge-peginsert-v0.1.1", 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:

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 Dataset	EXOKERN/contactbench-forge-peginsert-v0.1
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_v011_2026,
  title   = {EXOKERN ContactBench v0.1.1: Peg Insertion with Domain Randomization and Force/Torque},
  author  = {{EXOKERN}},
  year    = {2026},
  url     = {https://huggingface.co/datasets/EXOKERN/contactbench-forge-peginsert-v0.1.1},
  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-v0.1.1

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

Paper • 2408.04587 • Published Aug 8, 2024