yosubshin/aic-cable-insertion_sample

AIC Cable Insertion Dataset

About the AI for Industry Challenge

This dataset was collected for the AI for Industry Challenge (AIC), an open competition by Intrinsic (an Alphabet company) for developers and roboticists aimed at solving high-impact problems in robotics and manufacturing.

The challenge task is cable insertion — commanding a UR5e robot arm to insert fiber-optic cable plugs (SFP modules and SC connectors) into ports on a configurable task board in simulation (Gazebo). Policies must generalize across randomized board poses, rail positions, and plug/port types.

Competition Resources

• Event Page: intrinsic.ai/events/ai-for-industry-challenge
• Toolkit Repository: github.com/intrinsic-dev/aic
• Discussion Forum: Open Robotics Discourse

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Dataset Description

This dataset contains teleoperated demonstrations of cable insertion tasks recorded from the AIC Gazebo simulation environment as ROS 2 bag files (.mcap), converted to LeRobot v2.1 format for training Vision-Language-Action (VLA) policies.

Key Facts

Property	Value
Robot	UR5e (6-DOF) with impedance controller
Simulator	Gazebo (ROS 2)
Episodes	5
Cameras	3 wrist-mounted (left, center, right)
Camera Resolution	288×256 (downscaled from 1152×1024 at 0.25×)
FPS	20 Hz
Observation State	31-dim (TCP pose + velocity + error + joint positions + F/T wrench)
Action Space	6-dim Cartesian velocity (linear xyz + angular xyz)
Task Types	SFP module → NIC port, SC plug → SC port

Tasks

Each episode is labeled with a specific language instruction identifying the plug type, target port, and target rail:

Episode	Task Instruction
0	Insert the grasped SFP module into sfp_port_0 on the NIC card mounted on nic_rail_0
1	Insert the grasped SFP module into sfp_port_0 on the NIC card mounted on nic_rail_2
2	Insert the grasped SC plug into sc_port_base on SC port 1 mounted on sc_rail_1
3	Insert the grasped SC plug into sc_port_base on SC port 0 mounted on sc_rail_0
4	Insert the grasped SFP module into sfp_port_0 on the NIC card mounted on nic_rail_3

Scene Variation

Each trial features different randomization to encourage policy generalization:

Episode	Board Yaw (°)	Board Height (m)	Cable Type	Other Components Present
0 (Trial 1)	~25°	1.140	sfp_sc_cable	NIC cards on rail 0 & 1, SC mount, SFP mount
1 (Trial 2)	~45°	1.200	sfp_sc_cable	NIC card on rail 2, LC mount, SFP mount
2 (Trial 3)	~60°	1.300	sfp_sc_cable_reversed	SC ports on rail 0 & 1, SFP mount, SC mount, LC mount
3 (Trial 5)	~15°	1.110	sfp_sc_cable_reversed	SC port on rail 0, SFP mounts on both rails
4 (Trial 7)	~30°	1.100	sfp_sc_cable	NIC cards on rail 0 & 3, SC ports on both rails, LC mount, SFP mount

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Data Format and Features

Observation State (31-dim)

Index	Feature	Description
0–2	tcp_pose.position.{x,y,z}	TCP position in base frame
3–6	tcp_pose.orientation.{x,y,z,w}	TCP orientation (quaternion)
7–9	tcp_velocity.linear.{x,y,z}	TCP linear velocity
10–12	tcp_velocity.angular.{x,y,z}	TCP angular velocity
13–18	tcp_error.{x,y,z,rx,ry,rz}	Tracking error (current vs. reference)
19–24	joint_positions.{0–5}	Joint angles (shoulder_pan → wrist_3)
25–27	wrench.force.{x,y,z}	Wrist force-torque sensor (force)
28–30	wrench.torque.{x,y,z}	Wrist force-torque sensor (torque)

Action (6-dim Cartesian velocity)

Index	Feature	Description
0–2	linear.{x,y,z}	Cartesian linear velocity command
3–5	angular.{x,y,z}	Cartesian angular velocity command

Camera Views

Three wrist-mounted cameras provide stereo-like coverage of the insertion workspace:

• observation.images.left_camera — Left wrist camera (288×256 RGB)
• observation.images.center_camera — Center wrist camera (288×256 RGB)
• observation.images.right_camera — Right wrist camera (288×256 RGB)

Videos are stored as MP4 files (H.264, 20 fps).

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Dataset Structure

aic_lerobot_dataset/
├── data/
│   └── chunk-000/
│       ├── episode_000000.parquet
│       ├── episode_000001.parquet
│       ├── episode_000002.parquet
│       ├── episode_000003.parquet
│       └── episode_000004.parquet
├── meta/
│   ├── info.json
│   ├── tasks.jsonl
│   ├── episodes.jsonl
│   ├── episodes_stats.jsonl
│   └── stats.json
└── videos/
    └── chunk-000/
        ├── observation.images.left_camera/
        │   └── episode_00000{0-4}.mp4
        ├── observation.images.center_camera/
        │   └── episode_00000{0-4}.mp4
        └── observation.images.right_camera/
            └── episode_00000{0-4}.mp4

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Usage

Loading with LeRobot

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("shu4dev/aic-cable-insertion")

# Access a frame
sample = dataset[0]
print(sample["observation.state"].shape)   # torch.Size([31])
print(sample["action"].shape)              # torch.Size([6])

Loading with HuggingFace Datasets

from datasets import load_dataset

ds = load_dataset("shu4dev/aic-cable-insertion")
print(ds["train"][0])

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Data Collection

Demonstrations were collected via teleoperation in the AIC Gazebo simulation environment using the LeRobot integration (lerobot-record) with keyboard-based Cartesian control. The robot starts each trial with the cable plug already grasped and positioned within a few centimeters of the target port.

Raw ROS 2 bag data (.mcap files, 10–16 GB each) was converted to LeRobot v2.1 format using a custom streaming converter that:

1. Filters to only the 8 needed ROS topics (skipping TF, contacts, scoring)
2. Synchronizes all modalities to the center camera timestamps at 20 Hz
3. Extracts observation state from /aic_controller/controller_state, /joint_states, and /fts_broadcaster/wrench
4. Extracts actions from /aic_controller/pose_commands (Cartesian velocity mode)
5. Encodes camera streams as H.264 MP4 via direct ffmpeg pipe

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Intended Use

This dataset is intended for:

• Training imitation learning policies (ACT, Diffusion Policy, etc.)
• Training VLA models (π0, GR00T, OpenVLA, etc.) with language-conditioned cable insertion
• Benchmarking sim-to-sim transfer for contact-rich manipulation
• Research on fine-grained insertion tasks with force feedback

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Citation

If you use this dataset, please cite the AI for Industry Challenge:

@misc{aic2026,
  title={AI for Industry Challenge Toolkit},
  author={Intrinsic Innovation LLC},
  year={2026},
  url={https://github.com/intrinsic-dev/aic}
}

License

Apache License 2.0