Forge v1: 2968 expert episodes, 3 tasks, video + force/torque

README.md

@@ -1,3 +1,114 @@
 ---
-license: mit
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- robotics
+- isaac-lab
+- forge
+- manipulation
+- peg-insertion
+- force-torque
 ---
+
+# Forge v1 — contact-rich manipulation with force/torque, 3 tasks
+
+Expert demonstrations for the three [Isaac Lab](https://github.com/isaac-sim/IsaacLab) **FORGE**
+tasks, collected from trained `rl_games` PPO policies. Successful episodes only; the failures from
+the same collection are published separately as
+[`angledusgar/forge-failure-v1`](https://huggingface.co/datasets/angledusgar/forge-failure-v1).
+
+Beyond the usual LIBERO-shaped fields this dataset carries the **wrist force/torque signal**, which
+is what makes contact-rich insertion legible: the peg/hole clearance is 0.114 mm, far below what a
+224×224 camera can resolve, so the moment of contact and the onset of a jam are visible in the
+wrench long before they are visible in pixels.
+
+## Contents
+
+| task | episodes | steps/episode | prompt |
+|---|---|---|---|
+| `Isaac-Forge-PegInsert-Direct-v0` | ~984 | 149 | "insert the peg into the socket" |
+| `Isaac-Forge-GearMesh-Direct-v0` | ~996 | 299 | "mesh the gear onto the shaft" |
+| `Isaac-Forge-NutThread-Direct-v0` | ~988 | 449 | "thread the nut onto the bolt" |
+| **total** | **2,968** | — | 888,032 frames |
+
+All three tasks live in one repository, distinguished by the per-frame task string
+(`meta/tasks.jsonl`), the same packing `physical-intelligence/libero` uses.
+
+## Features
+
+| key | dtype | shape | meaning |
+|---|---|---|---|
+| `image` | video (AV1) | 256×256×3 | fixed third-person camera, one per environment |
+| `wrist_image` | video (AV1) | 256×256×3 | wrist camera, mounted on `panda_hand` |
+| `state` | float32 | (8,) | `eef_pos` (3) + axis-angle from `eef_quat` (3) + gripper joints (2) |
+| `actions` | float32 | (7,) | the executed Forge environment action |
+| **`ft`** | float32 | (6,) | wrench at the wrist in the EE frame — force (3) then torque (3), smoothed |
+| **`ft_noisy`** | float32 | (3,) | the force the policy actually observed, with Forge's observation noise |
+
+`state` is deliberately 8-D and named as in the openpi LIBERO conversion, so
+`LeRobotLiberoDataConfig` can be reused unchanged; `ft` / `ft_noisy` are extra keys that openpi's
+`RepackTransform` simply drops. Robot is a Franka Panda; fps 15 (Forge control rate); LeRobot
+dataset format `v2.1`.
+
+Two force channels are kept on purpose: a detector can be *trained* on the clean wrench while being
+*evaluated* on the noisy signal the robot really had.
+
+## How it was collected
+
+Sixteen environments were stepped in lockstep — Factory/Forge resets all environments together, so
+each window yields exactly N equal-length episodes — with the expert acting deterministically.
+Episodes were kept regardless of outcome and split by result afterwards.
+
+| task | expert checkpoint | success rate |
+|---|---|---|
+| PegInsert | 300-epoch PPO, trained 2026-06-30 on the **stock** task | ~96% |
+| GearMesh | 300-epoch PPO, trained 2026-08-01 | ~99% |
+| NutThread | 300-epoch PPO, trained 2026-08-01 | ~98% |
+
+## ⚠️ Reproducing PegInsert
+
+The PegInsert episodes were collected under **stock Forge dynamics**, but the Isaac Lab checkout
+used for collection has `ForgeTaskPegInsert` modified for a separate study (higher and wider spawn,
+plus a ±10° tilt of the peg *inside the gripper* to induce jams). Collection therefore overrode six
+knobs back to their stock values:
+
+```python
+hand_init_pos            = [0.0, 0.0, 0.047]
+hand_init_pos_noise      = [0.02, 0.02, 0.01]
+hand_init_orn_noise      = [0.0, 0.0, 0.785]
+held_asset_pos_noise     = [0.003, 0.0, 0.003]
+fixed_asset_init_tilt_deg = 0.0
+held_asset_tilt_deg       = 0.0
+```
+
+On an unmodified Isaac Lab these are already the defaults, so **run it as-is**. GearMesh and
+NutThread were never modified. This matters: the 2026-06-30 PegInsert expert scores ~96% on stock
+dynamics and **0/12** on the modified variant, and a policy trained on the modified variant plateaus
+at a reward of 72.7 against 345.5 on stock — the same task name, two different problems.
+
+## Loading
+
+```python
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+ds = LeRobotDataset("angledusgar/forge-v1")
+sample = ds[0]
+sample["image"]      # (3, 256, 256)
+sample["ft"]         # (6,)  force + torque
+```
+
+If video decoding fails with a `torchcodec` import error, the machine is missing FFmpeg shared
+libraries; pass `video_backend="pyav"` or install ffmpeg.
+
+## Known gaps
+
+- **Arm joint angles are not included.** Only the two gripper joints were recorded; `state`
+  follows the LIBERO convention, which is end-effector-based. Adding `joint_pos` requires
+  re-collection and is planned for a future version.
+- One shard was lost to a corrupted HDF5 file (a writer killed mid-write), costing at most 47
+  PegInsert episodes out of ~1,010 collected for that task.
+- Success rates are high by construction, so the failure set is small; see
+  `forge-failure-v1` for the 51 failures and read its card before treating them as a balanced
+  negative set.

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