Initial release: card

README.md

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+---
+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.