EXOKERN/contactbench-forge-peginsert-v0

EXOKERN ContactBench v0 — Peg Insertion with Force/Torque

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

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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 (Force-Guided Exploration) framework in NVIDIA Isaac Lab. Every frame captures what the robot feels, not just what it sees.

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Dataset Overview

Metric	Value
Episodes	2,221
Total Frames	330,929
Avg Episode Length	~149 steps
Control Frequency	20 Hz
Format	LeRobot v3.0 (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

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Features

Each frame contains the following tensors:

Feature	Shape	Description
observation.state	(24,)	Flattened observation vector — see State Tensor Semantics below
observation.wrench	(6,)	6-axis force/torque: [Fx, Fy, Fz, Mx, My, Mz] — see 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

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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 (indices 0–15) extended by the FORGE force-sensing additions (indices 16–23). See Noseworthy et al., 2024 §III for details.

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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.

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Quick Start

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

from datasets import load_dataset

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

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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.

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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)

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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

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Reproduction

Methodology documentation available upon request. Visit exokern.com for details.

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Related Work

This dataset builds on the following research:

• 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)
• LeRobot — Cadene et al., "LeRobot: State-of-the-art Machine Learning for Real-World Robotics" (2024)

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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 for enterprise inquiries.

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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

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Citation

@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}
}