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aic-cable-insertion_sample

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metadata 
license: apache-2.0
task_categories:
  - robotics
tags:
  - lerobot
  - robotics
  - cable-insertion
  - manipulation
  - imitation-learning
  - vision-language-action
  - intrinsic
  - ai-for-industry-challenge
  - ur5e
  - sim-to-real
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/**/*.parquet

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

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

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).

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

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])

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

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

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