| --- |
| pretty_name: ForceBenchmark WeightSorting V6 |
| tags: |
| - robotics |
| - imitation-learning |
| --- |
| |
| # WeightSorting V6 / `ForcebenchmarkPandaWeightSorting-v2` |
|
|
| This release contains the 1,000 successful, physically paired demonstrations |
| used for the 100k-step diffusion-policy experiments. There are 500 scene pairs: |
| one light and one heavy rollout per scene. The task uses four masses (0.2, 0.4, |
| 0.8, and 1.0 kg), balanced at 250 episodes each. The pair-disjoint split has |
| 450 training pairs and 50 validation pairs. The reported 100k training jobs |
| used all 500 pairs; the split is supplied for future analysis. |
|
|
| Dataset: [HanLinqi/ForceBenchmark-WeightSorting-v6](https://huggingface.co/datasets/HanLinqi/ForceBenchmark-WeightSorting-v6). |
|
|
| ## Files |
|
|
| | HF path | Contents | SHA-256 of H5 | |
| | --- | --- | --- | |
| | `planning_data/forcebenchmarkpandaweightsorting_v2/trajectory_rgb.h5` | 18D state (`qpos+qvel`), RGB, action, causal wrist wrench, auxiliary labels | `e17010001cb5e47b64e84b93b9b371f45c96015bf74a2c73265459b68920227b` | |
| | `planning_data_26d/forcebenchmarkpandaweightsorting_v2/trajectory_rgb.h5` | Same rollouts with 26D state (`qpos+qvel+TCP+gripper`) | `1c10151e66cf3f8aadd9ddc4b8d5e0c6637547f36b15564a20f842ff8fe1f5d4` | |
|
|
| Each H5 has a matching `trajectory_rgb.json`. The state18 directory also has |
| `pair_splits.json`, `decision_windows.json`, and `provenance/` with the raw |
| collection summary, audit and gate. `phase_id`, `stage_id`, scene |
| pair IDs, mass and route labels are auxiliary targets or audit metadata; they |
| are not policy observations. The raw audit state is not in either H5. |
|
|
| ## Task and data construction |
|
|
| Import `task_force.forcebenchmark_panda_weight_sorting_v2` to register the |
| environment. The collector is |
| `task_force/scripts/generate_weight_sorting_v2_physical_pairs_v6.py`; its |
| campaign driver, audit, merge and conversion scripts are in `task_force/scripts`. |
| See `task_force/docs/weightsorting_v6_physical_pairs.md` for the full task |
| definition, collection protocol and physical assumptions. |
|
|
| The V2 action is a 7D increment to a persistent Cartesian reference. It is |
| incompatible with V1's instantaneous-TCP action semantics. The task uses an |
| ideal rigid grasp latch after physical finger contact and finite-precision |
| synthetic policy sensors; it does not represent ordinary friction-only |
| pinching or raw simulator proprioception. The same online sensors and |
| controller must be used for force and no-force policies. Online wrist wrench |
| settings are `force_torque_source="incoming_joint"`, |
| `force_substep_mean=True`, and `zero_force_on_reset=True`. |
|
|
| Collection audited exact shared pre-decision actions and reference positions, |
| causal wrench, sensor replay, physical pair separation and five-fold |
| scene-grouped leakage probes. Collection accepted 1,000 episodes/500 pairs. |
| The separate pilot used evaluation seeds 0–4, 20 episodes per seed. For each |
| trained experiment, use five evaluation seeds ×20 episodes per seed, with |
| evaluation scenes separate from collection scenes. |
|
|
