model card

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 · carrot-in-pot · EEF-delta (state 16 / action 7) — checkpoint 10k
+
+Action Chunking Transformer trained on the **real** UR7e *"Put carrot in pot"* demonstrations
+(54 GELLO-teleop takes, 30 fps) in the **EEF-delta action space** (`eef_delta_v1`). This is the
+**10k-step checkpoint**, picked as the least-overfit of 10k..50k — all were within 0.04 mm of each
+other on the open-loop metric.
+
+Joint-space siblings for this task live in the sim evaluation (`sim_collect/eval`), the IFQL
+policy in [`Bigenlight/carrot-in-pot-ifql`](https://huggingface.co/Bigenlight/carrot-in-pot-ifql).
+
+## Observation / action space (`eef_delta_v1`)
+- `observation.state` **16-D** = `[q1..q6 (rad, UR order), tcp_x, tcp_y, tcp_z (m, base_link),
+  r11, r21, r31, r12, r22, r32 (first two columns of the TCP rotation — continuous 6-D rep),
+  grip_pos (0=open..1=closed)]`. TCP = `ur_kin.fk(q)` (base_link→tool0) + 0.174 m along flange +Z.
+- `action` **7-D** = `[dx, dy, dz, drx, dry, drz, grip_cmd]`: the **achieved** TCP motion between
+  consecutive 30 fps frames (`dp = p_{t+1}-p_t`, `drot = so3_log(R_{t+1} R_t^T)`, base frame,
+  rotation left-multiplied), gripper = absolute recorded command 0..1. Deploy inverts it exactly
+  (`p_target = p_live + dp`, `R_target = so3_exp(drot) R_live`, analytic IK with branch locking).
+- Cameras: `observation.images.cam1` (scene), `cam2` (wrist), RGB 720×1280 in the dataset,
+  **resized to 360×640 at train time** (`image_transforms.resize`) — resize the same way at inference.
+- Backbone ResNet18 (ImageNet), `chunk_size = n_action_steps = 100`, MEAN_STD normalization,
+  ~51.6M params.
+
+## Training
+- Dataset: `carrot_in_pot_eef_lerobot_v3` — a local LeRobot v3 re-export of
+  [`Bigenlight/carrot_in_pot_lerobot_v3`](https://huggingface.co/datasets/Bigenlight/carrot_in_pot_lerobot_v3)
+  (54 episodes / 17,085 frames after dropping the stale tail; joints shifted by the recorder's
+  per-take τ≈0.90 s cache lag and linearly re-interpolated). The EEF re-export is **not yet on
+  the Hub** (train_config names it `Bigenlight/carrot_in_pot_eef_lerobot_v3`).
+- `lerobot-train`, batch 8, seed 1000, 50k steps configured (`save_freq` 10k), `eval_split 0.111`
+  (held-out episodes 48–53), single RTX A4000 (kanu). Job `act_carrot_eef`.
+
+## Held-out results (open-loop, episodes 48–53, k=30)
+| checkpoint | pos MAE | grip acc | chunk-30 cumulative error |
+|---|---|---|---|
+| **10k (this)** | 0.82–0.86 mm (all ckpts) | 0.95 | 36.6–38.0 mm vs 65.6 mm zero-motion baseline |
+
+All checkpoints 10k–50k are statistically indistinguishable on this metric; 10k was chosen as
+least-overfit. lerobot's own `eval_loss` is computed on un-resized 720p and was **not** used.
+
+## Status
+Real-robot closed-loop evaluation: **not yet run** (the deploy path is EEF mode of
+`gello_policy/policy_leader_node` + `eef_space.apply_delta`; the shipped ZMQ servers are
+joint-space 7/7 and refuse this checkpoint's 16-D state). Provenance: gello_software branch
+`feat/carrot-eef-il` (converter `scripts/dataset/convert_carrot_to_lerobot_eef.py`, validator 71/71 PASS).