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.
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.