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+---
+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)](https://www.intrinsic.ai/events/ai-for-industry-challenge), 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](https://www.intrinsic.ai/events/ai-for-industry-challenge)
+- **Toolkit Repository**: [github.com/intrinsic-dev/aic](https://github.com/intrinsic-dev/aic)
+- **Discussion Forum**: [Open Robotics Discourse](https://discourse.openrobotics.org/c/competitions/ai-for-industry-challenge/)
+
+---
+
+## 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
+
+```python
+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
+
+```python
+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

README.md

@@ -0,0 +1,211 @@
+---
+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)](https://www.intrinsic.ai/events/ai-for-industry-challenge), 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](https://www.intrinsic.ai/events/ai-for-industry-challenge)
+- **Toolkit Repository**: [github.com/intrinsic-dev/aic](https://github.com/intrinsic-dev/aic)
+- **Discussion Forum**: [Open Robotics Discourse](https://discourse.openrobotics.org/c/competitions/ai-for-industry-challenge/)
+
+---
+
+## 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
+
+```python
+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
+
+```python
+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