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README.md

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+---
+license: cc-by-nc-4.0
+library_name: lerobot
+pipeline_tag: robotics
+tags:
+  - robotics
+  - lerobot
+  - act
+  - imitation-learning
+  - ur7e
+  - end-effector
+---
+
+# ACT · banana-in-pot · EEF (10-D) — checkpoint 40k
+
+Action Chunking Transformer (ACT) trained on the **end-effector (EEF) action space**
+for the task *"put the right banana in the pot"* (UR7e arm, GELLO teleoperation,
+LeRobot v3.0). This is the **40k-step checkpoint**, selected as best by open-loop MAE.
+
+This is the EEF counterpart of the joint-space model
+[`Bigenlight/act_banana_in_pot`](https://huggingface.co/Bigenlight/act_banana_in_pot).
+
+## Action / observation space
+- `observation.state` / `action`: **10-D** = `[x, y, z, r1..r6 (Zhou 6D rotation), gripper]`
+  — absolute next-frame TCP pose (xyz in metres) + gripper. (The joint model uses 7-D
+  `[q1..q6, gripper]`.)
+- Cameras: `observation.images.cam1`, `observation.images.cam2` (RGB, resized 360×640).
+- Backbone: ResNet18 + VAE, `chunk_size=100`, ~51.6M params. Normalization: MEAN_STD.
+
+## Training
+- Recipe identical to the joint baseline `train_act_valdiag.sh` except dataset + steps:
+  `--dataset.eval_split=0.117` (held-out episodes 45–50), batch 8, seed 1000, 50k steps.
+- Dataset: `banana_in_pot_ee_action` (51 eps / 21,524 frames, 30 fps), built from the raw
+  [`Bigenlight/banana_in_pot_raw`](https://huggingface.co/datasets/Bigenlight/banana_in_pot_raw)
+  via recorded `tcp_pose` (no FK needed).
+- Hardware: single RTX A4000, ~2h43m. No overfitting (held-out eval_loss monotone to 0.4594@50k).
+
+## Held-out results (open-loop, eps 45–50)
+| checkpoint | pose MAE (m + 6D) | gripper acc |
+|---|---|---|
+| **40k (this)** | **0.05564** | **0.914** |
+| 50k | 0.05564 | 0.911 |
+
+Selected by open-loop MAE (repo convention), not by eval_loss.
+
+> ⚠️ Note: EEF pose MAE mixes metres (xyz) and unitless 6D-rotation and is **not**
+> directly comparable to the joint model's radian MAE. See the comparison writeup.
+
+## Usage
+```python
+from lerobot.policies.act.modeling_act import ACTPolicy
+policy = ACTPolicy.from_pretrained("Bigenlight/act_banana_in_pot_ee")
+```
+
+## Links
+- Experiments repo, full report, reproducibility (Docker): GitHub
+  [`Bigenlight/banana-in-pot-experiments`](https://github.com/Bigenlight/banana-in-pot-experiments)
+  — see `docs/ACT_EE_RESULTS.md` and `docs/JOINT_VS_EEF_ACT_COMPARISON.md`.
+
+*License: CC-BY-NC-4.0 (trained on real-lab teleoperation video).*