| --- |
| 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. |
|
|
