| --- |
| license: apache-2.0 |
| library_name: lerobot |
| pipeline_tag: robotics |
| tags: |
| - LeRobot |
| - act |
| - action-chunking-transformer |
| - imitation-learning |
| - real-robot |
| datasets: |
| - SIRLab-HGU/indy7-act-spatial-coverage |
| --- |
| |
| # Indy7 ACT spatial-coverage policies |
|
|
| Twelve Action Chunking Transformer (ACT) policies trained on a real Neuromeka |
| Indy7 arm to grasp a vertical PVC-U pipe from a single fixed overhead RGB camera. |
| They are the models behind a study of how the *spatial spread* of demonstrations |
| affects a visuomotor policy. |
|
|
| - Code, protocol and results: https://github.com/SIRLab-RobotArm/indy7-act-spatial-coverage |
| - Training data: https://huggingface.co/datasets/SIRLab-HGU/indy7-act-spatial-coverage |
| - Paper: not published yet; this card will be updated with the DOI |
|
|
| ## The twelve models |
|
|
| Four training conditions x three seeds (0, 1, 2), each trained for exactly |
| 100,000 optimisation steps with LeRobot's ACT implementation (v0.5.1). |
|
|
| | Condition | Demonstrations | Spread | Success over 120 trials | |
| | --- | ---: | --- | ---: | |
| | A | 60 | one position | 12.5% | |
| | B | 60 | three positions | 61.7% | |
| | C | 60 | nine positions | 80.0% | |
| | D | 180 | nine positions | 96.7% | |
|
|
| At positions never demonstrated, condition A succeeded 0 times out of 60, while |
| condition D matched its trained-position rate at 96.7%. The full 480-trial table, |
| the blinding scheme and the analysis plan are in the code repository. |
|
|
| ## Inputs and outputs |
|
|
| - Input: one 240 x 320 RGB image from the overhead camera, plus a 10-dimensional |
| state (end-effector x, y, z in mm; six joint angles in rad; gripper command). |
| - Output: a chunk of 4-dimensional actions (end-effector delta x, y, z in mm; |
| gripper command), consumed at 5 Hz. |
|
|
| The policies are tied to this exact camera placement, table, object and robot. |
| They are a reference point for reproducing the study, not a general grasping |
| model. |
|
|
| ## How they were trained |
|
|
| Trained on the PNG version of the dataset. The published dataset is a video |
| re-encode of it, so retraining from the Hub copy will differ slightly; see the |
| dataset card for the measured pixel difference. |
|
|
| Each condition's episode membership was frozen and checksummed before training, |
| and the evaluation ran under blind model codes. |
|
|
| Each checkpoint keeps the `train_config.json` it was trained with, including the |
| absolute paths of the machine it ran on. That is left in place as provenance; it |
| holds no credentials, and Weights & Biases logging was disabled. |
|
|
| ### Machine and versions |
|
|
| | Component | Value | |
| | --- | --- | |
| | GPU | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 96 GB, driver 595.84 | |
| | CPU / RAM | AMD Ryzen 7 9800X3D, 16 threads / 123 GB | |
| | OS | Ubuntu 24.04.4 LTS, kernel 6.8.0 | |
| | Python | 3.12.13 | |
| | PyTorch | 2.10.0+cu130, CUDA 13.0 | |
| | LeRobot | 0.5.1 | |
|
|
| ## Safety |
|
|
| Running these policies moves a six-axis industrial arm. Keep the workspace clear, |
| keep an emergency stop within reach and never run them unattended. The evaluation |
| harness in the code repository enforces workspace bounds, a per-step limit and a |
| command watchdog; do not bypass them. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @software{kim_indy7_act_spatial_coverage_2026, |
| author = {Kim, Yuyoung and Kim, Dogyung and Yoo, Jaehyeok and |
| Kim, Dongyun and Kim, Dowook and Lee, WonHyong}, |
| title = {Spatial coverage versus sample count in visuomotor imitation |
| learning: an RGB-only ACT study on a real Indy7 arm}, |
| year = {2026}, |
| url = {https://github.com/SIRLab-RobotArm/indy7-act-spatial-coverage} |
| } |
| ``` |
|
|
| The paper citation will be added here once it is published. |
|
|
| ## License |
|
|
| Apache License 2.0. Free to use, modify and redistribute, including |
| commercially, keeping the notices and stating your changes; it also grants the |
| patent rights of the contributors. Full text: |
| https://www.apache.org/licenses/LICENSE-2.0 |
|
|
| The training data is released separately under CC BY 4.0; see the dataset card. |
|
|
