angledusgar/forge-failure-v1
Forge failure v1 — the episodes the expert lost The failed episodes from the same collection that produced angledusgar/forge-v1: identical policies, identical environments, identical recording — only the outcome differs. Same schema, so the two sets can be mixed or contrasted directly. They are published apart from the successes on purpose. Imitation learning on failed trajectories teaches failing, so a behaviour-cloning consumer should never receive them by accident; a failure… See the full description on the dataset page: https://huggingface.co/datasets/angledusgar/forge-failure-v1.
Forge failure v1 — the episodes the expert lost
The failed episodes from the same collection that produced `angledusgar/forge-v1`: identical policies, identical environments, identical recording — only the outcome differs. Same schema, so the two sets can be mixed or contrasted directly.
They are published apart from the successes on purpose. Imitation learning on failed trajectories teaches failing, so a behaviour-cloning consumer should never receive them by accident; a failure detector, on the other hand, cannot be trained or calibrated without them.
Contents
Failures are rare because the experts are good: roughly 4% for PegInsert and 1% for GearMesh and NutThread, out of ~1,000 episodes each. Collecting 100 failures per task at those rates would need several thousand episodes per task.
These are tail events, not a representative failure distribution. They are what a near-ceiling policy happens to get wrong, which is not the same as the failure modes an intervention system is usually designed against. For a set where failure is common and mechanically meaningful — jams from a peg cocked in the gripper — the task has to be made harder at collection time rather than sampled from a policy that rarely fails.
Features
Identical to forge-v1:
The wrench channels are the point of this set: on a 0.114 mm clearance the difference between approaching, contacting and binding is not visible at 224×224, but it is plain in the force trace.
Provenance and caveats
Collection recipe, expert checkpoints, and the PegInsert stock-override caveat are documented in the `forge-v1` card and apply unchanged here.
Two notes specific to this set:
- Per-episode success labels are implicit. Every episode here failed; there is no
successcolumn, since a mixed set would need one and this one does not. - The first collection run lost its in-memory failures. Failures were buffered and only flushed once enough accumulated, and that run was killed under memory pressure before the flush. The count here reflects what survived; roughly 19 failures from the first partial run were lost.