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 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.
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:
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
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;
statefollows the LIBERO convention, which is end-effector-based. Addingjoint_posrequires 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-v1for the 51 failures and read its card before treating them as a balanced negative set.