Add dataset card

README.md

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+---
+license: mit
+pretty_name: FailBench RoboCasa v2 (contact prediction)
+task_categories:
+- robotics
+tags:
+- robotics
+- manipulation
+- failure-detection
+- contact-prediction
+- mujoco
+- robocasa
+- franka-panda
+size_categories:
+- 10K<n<100K
+---
+
+# FailBench RoboCasa v2 — contact-prediction dataset
+
+Labeled robot-failure trials built on **RoboCasa** kitchen demos (PandaMobile / Franka).
+Each trial injects a hardware failure partway through a teleop/MimicGen demo, then records the
+**contacts the failure causes** during a 1-second settle. The supervised target is a
+**240×320 force-weighted contact heatmap** in the agentview camera — the model learns to predict
+*where* a failure at a given pre-failure configuration will drive the robot/objects into contact.
+
+Sibling dataset: [`aaronngx/failbench-libero-v2`](https://huggingface.co/datasets/aaronngx/failbench-libero-v2)
+(identical schema, 240×320, LIBERO scenes). The two pool cleanly for cross-corpus training.
+
+- **39,689 train trials** across **20 task files** (10 tasks × {human demos, MimicGen synthetic}).
+- Also ships a few **held-out task files** (`TurnOffStove`, `TurnOnMicrowave`, `TurnOnStove_mg`) for
+  generalization eval — these are NOT in `manifest_train.csv`.
+- One HDF5 per task under `v2/`; per-trial groups at `/trials/<trial_id>/`.
+
+## ⚠️ The one gotcha: `import hdf5plugin` first
+
+Arrays are **Blosc(lz4)-compressed** (third-party HDF5 filter id **32001**). Any reader must
+`import hdf5plugin` **before** `h5py` opens a file, or reads fail with *"can't open plugin
+directory"*. (`dataset.compression` reports `None` for this filter — misleading; it IS compressed.)
+`pip install hdf5plugin` and import it first. The bundled `load_failbench.py` does this for you.
+
+## Quick start
+
+```bash
+pip install -r requirements.txt
+python -c "from huggingface_hub import snapshot_download as s; \
+  s('aaronngx/failbench-robocasa-v2', repo_type='dataset', local_dir='failbench-robocasa-v2')"
+
+# load a trial + rebuild its contact target (pure h5py+numpy+scipy, no FailBench/MuJoCo):
+python load_failbench.py --data_root failbench-robocasa-v2 \
+                         --cache_root failbench-robocasa-v2/target_cache
+```
+
+### Standalone load snippet
+
+```python
+import load_failbench as fb                       # imports hdf5plugin for you
+df = fb.load_manifest("failbench-robocasa-v2")     # the train manifest, as a DataFrame
+row = df[df.n_contacts > 0].iloc[0]
+trial = fb.read_trial(fb.resolve_h5("failbench-robocasa-v2", row.split, row.task), row.trial_id)
+
+# (240,320) force-weighted contact heatmap = the prediction target:
+target = fb.heatmap_from_projection("failbench-robocasa-v2/target_cache",
+                                    row.split, row.task, row.trial_id)   # filtered (training)
+# or rebuild from raw contacts + camera, no cache:  fb.heatmap_from_contacts(trial)
+```
+
+## Repo layout
+
+```
+v2/<task>.h5                 # 22 task files (human; *_mg.h5 = MimicGen)
+v2/manifest_train.csv        # 39,689 train trials (use THIS for training)
+v2/manifest.csv              # full build incl. held-out + (pre-quarantine) rows
+v2/quarantine.csv            # excluded trial ids
+target_cache/robocasa/<task>.h5   # per-trial /<trial_id>/projection (N,3)[u,v,force] + failure_prob attr
+load_failbench.py  requirements.txt  examples/quickstart.py
+planner/  scripts/           # the exact FailBench load+train code subtree (see "Train")
+```
+
+**Resolve files by `(split, task)` relative to your local root** — the manifest's `h5_path`
+column holds the original build machine's absolute path and is not portable.
+
+## Per-trial schema
+
+Each `/trials/<trial_id>/` group. `T=8` window @ 4 fps; settle `S=50` steps (~1 s). Images are
+**(H,W)=(240,320)**; depth is float16 metric.
+
+| Key | Shape | Dtype | Meaning |
+|---|---|---|---|
+| `window_agentview_rgb` | (8,240,320,3) | u8 | pre-failure agentview window |
+| `window_agentview_depth` | (8,240,320) | f16 | depth, metres |
+| `window_wrist_rgb` / `_depth` | (8,240,320[,3]) | u8/f16 | wrist (`robot0_eye_in_hand`) window |
+| `window_qpos` / `window_qvel` | (8,7) | f32 | arm joint pos/vel over window |
+| `window_ee_pos` | (8,3) | f32 | end-effector xyz |
+| `window_gripper_ctrl` | (8,·) | f32 | gripper command |
+| `pre_rgb` / `pre_depth` | (240,320[,3]) | u8/f16 | single pre-failure frame (v1-compat) |
+| `pre_qpos`/`pre_qvel`/`pre_ee_pos` | (7,)/(7,)/(3,) | f64 | pre-failure state |
+| `goal_qpos`/`goal_qvel` | (k,7) | f32 | goal-conditioning frames |
+| `contact_positions` | (N,3) | f32 | **world-frame** contact points during settle |
+| `contact_force_world` | (N,3) | f32 | linear force, world frame (magnitude → heatmap weight) |
+| `contact_forces` | (N,6) | f32 | contact-frame wrench (legacy; first 3 = linear) |
+| `contact_time` | (N,) | i32 | settle step the contact occurred |
+| `contact_geom_pairs` | (N,2) | i32 | colliding geom ids |
+| `contact_failure_id` | (N,) | i32 | which failure caused it |
+| `baseline_contact_geom_pairs` / `_positions` | (M,·) | i32/f32 | healthy-hold replay contacts (filter input) |
+| `geom_bodyid` | (ngeom,) | i32 | geom→body map (body-level filter) |
+| `robot_geom_ids` (attr) | (·,) | i32 | robot geom ids |
+| `post_agentview_rgb`/`_depth`, `post_wrist_*` | (240,320[,3]) | u8/f16 | post-settle observation |
+| `cam_agentview_pos`/`_mat0`/`_fovy`/`_size` | (3,)/(9,)/()/(2,) | f64/i32 | agentview pinhole calibration |
+| `settle_qpos`/`qvel`/`gripper_qpos` | (50,·) | f32 | post-failure state trajectory |
+| `settle_obj_pos`/`settle_obj_quat` | (50,nobj,·) | f32 | object pose trajectory |
+| `obj_names` | (nobj,) | str | object body names |
+
+Scalar attrs: `trial_id, split(="robocasa"), task, demo_key, seed, fail_idx, traj_progress,
+failure_mode, failure_prob, is_holding`.
+
+### Failure modes
+`GRIPPER_OPEN`, `SINGLE_JOINT`, `MULTI_JOINT`, `ALL_JOINTS`, `SLIPPERY_GRIP`. Each trial samples
+one mode at a stratified `traj_progress` (failure fraction along the demo); `failure_prob` is the
+mode's prior, used as the per-trial heatmap weight.
+
+### Camera
+| Role | Camera | W×H |
+|---|---|---|
+| **agentview (target grid)** | `robot0_agentview_center` | **320×240** |
+| wrist | `robot0_eye_in_hand` | 320×240 |
+
+The contact heatmap is projected into `robot0_agentview_center`, so the (240,320) target IS the
+prediction grid. For live eval, render that camera at 320×240 so recorded contacts line up with
+the prediction. (LIBERO uses `agentview`/`eye_in_hand` — same resolution/semantics.)
+
+## The target
+
+`target_cache/robocasa/<task>.h5[<trial_id>]/projection` is `(N,3)=[u,v,force_mag]` of the
+**failure-induced** in-frame contacts (static/baseline resting contacts removed by a two-stage
+filter), plus a `failure_prob` attr. Rebuild the dense heatmap as
+`scatter(force·failure_prob) → Gaussian blur(σ=4 px)` (`load_failbench.heatmap_from_projection`,
+or `planner.risk.v2_targets.build_target_from_projection` on GPU). Pass `log1p=True` to match the
+GPU training path's mass compression.
+
+## Train (reproduce the in-repo benchmark model)
+
+The bundled `planner/` + `scripts/benchmark/train_one.py` is the exact load+train subtree (pure
+torch+torchvision; no MuJoCo). Targets are built on the fly — there is **no** `--target_cache_root`
+flag.
+
+```bash
+PYTHONPATH=. python -m scripts.benchmark.train_one \
+    --robocasa_v2_root failbench-robocasa-v2/v2 \
+    --model unet --modalities state rgb --T 8 \
+    --split_by demo --epochs 10 --batch_size 64
+# models: mlp | convdec | unet | transformer ;  modalities: state goal rgb depth failure_mode ...
+# pooled cross-corpus: add --v2_root <libero v2 root> --splits libero_spatial libero_object libero_goal
+```
+
+## License & attribution
+
+Released under **MIT**. Built on **RoboCasa** (Nasiriany et al.), **MimicGen** (Mandlekar et al.),
+and **robosuite** — please cite those works. Contact labels and the failure-injection pipeline are
+from **FailBench**. Underlying demo content remains under its upstream RoboCasa license.