NeDM — Neural Reduced Dynamics Datasets

High-fidelity Project Chrono trajectories used to train the neural reduced dynamics models (NN-ROMs) in

Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control Harry Zhang and Dan Negrut, 2026 (preprint). Project page: https://uwsbel.github.io/NeDM/ · Code: https://github.com/uwsbel/NeDM

Every dataset here is exactly what the paper's models were trained and validated on. Two tiers are published (70 GB in total):

• raw/ — every recorded channel of every episode (Parquet, float32), plus a per-episode index and the byte-exact collection metadata (driver profiles, seeds, terrain, termination causes). This is the reusable resource: build your own reduced states from it.
• processed/ — the four training caches the deployed models read (.npy), so the paper's training configs run without touching the raw data.

Datasets

Config	System	Terrain / task	Rate	Episodes (train / val)	Rows	Raw Parquet	Columns
hmmwv_flat	HMMWV (HMMWV_Full, TMEASY tires, SMC contact)	flat rigid, μ = 0.9, 900 × 900 m	100 Hz	32,768 (26,124 / 6,644)	160,551,861	44.0 GB, 128 shards	105
hmmwv_bumpy	HMMWV (same vehicle)	rigid heightmap, 100 random 500 × 500 m fields, ±0.6 m	100 Hz	1,360 (1,104 / 256)	4,511,778	1.3 GB, 4 shards	105
hmmwv_crm	HMMWV (rigid-mesh tires)	CRM deformable soil (SPH), 150 × 150 × 0.25 m	100 Hz	2,000 (1,582 / 418)	2,884,961	0.8 GB, 4 parts	105
arm	4-DOF LRV arm mounted on an M113 (base held)	free-space joint motion, PD torque control	50 Hz	15,000 (12,716 / 2,284)	920,640	0.09 GB, 15 shards	47
tracked	M113 tracked vehicle, arm welded at home	flat rigid drive, 10 manoeuvre families	50 Hz	2,160 (1,808 / 352)	1,683,484	0.2 GB, 60 shards	42

Roles in the paper: hmmwv_flat + hmmwv_crm train the terrain-conditioned HMMWV NN-ROM (Study Case I); hmmwv_bumpy is the zero-shot out-of-distribution test regime and never enters training, model selection, normalisation or reward tuning; tracked and arm train the two Study Case II NN-ROMs. All five were collected with PyChrono 10.0.0 (conda projectchrono channel) using the collectors in the code repository (src/nedm/hmmwv_data.py, scripts/collection/collect_hmmwv_crm_dataset.py, src/nedm/arm_data.py, src/nedm/tracked_vehicle_data.py).

Splits

Train/val is decided per episode at collection time and stored in the split column: sha1(episode_id)[:8] / 0xFFFFFFFF < validation_ratio → val (ratio 0.20 for the HMMWV sets, 0.15 for arm and tracked). Whole episodes stay together; the assignment depends only on the episode id, so it is stable under re-sharding. train and val files never share an episode.

Layout

raw/<config>/train/<shard>.parquet     transitions, one file per raw collection shard
raw/<config>/val/<shard>.parquet
raw/<config>/episodes.parquet          one row per episode: index entry + JSON sidecar (see below)
raw/<config>/metadata.tar.gz           byte-exact originals: dataset_index.json, collector_config.resolved.json,
                                       episodes/<id>.json sidecars, shard-plan manifests
processed/<cache>/                     .npy training caches + metadata.json (state layout, normalisation)
assets/bumpy_terrain/bumpy_field_NNN.bmp   the 100 heightmaps behind hmmwv_bumpy (256×256, 8-bit, gray 128 = 0 m)
release_manifest.json                  sha256 / size / row count of every file, tool versions, source commit

Rows are ordered by episode (collection order) then sample_index; each episode is contiguous inside exactly one file. Column names and order are the collector's CSV columns, unchanged. All physical channels are float32 (time_s is float64; sample_index, collision are int32; identifiers are dictionary-encoded strings). Files are zstd-compressed with BYTE_STREAM_SPLIT float encoding and ≤ 262,144-row row groups.

Column groups

HMMWV (hmmwv_flat, hmmwv_bumpy, hmmwv_crm — identical 105 columns). Units are in the names (_m, _mps, _mps2, _rad, _radps, _n, _nm); world frame is Chrono's ISO (x forward, z up), body frame is the chassis frame.

Group	Columns
identifiers	episode_id, scenario_name, scenario_family, split, sample_index, time_s
driver command (the action)	driver_steering ∈ [−1, 1], driver_throttle ∈ [0, 1], driver_braking ∈ [0, 1]
chassis pose	pos_{x,y,z}_m, quat_e0..e3, roll_rad, pitch_rad, yaw_rad
chassis motion	vel_world_{x,y,z}_mps, vel_body_{x,y,z}_mps, acc_world_*, acc_body_*, ang_vel_world_{x,y,z}_radps, ang_vel_body_{x,y,z}_radps, speed_mps, body_slip_rad, roll_rate_radps, yaw_rate_radps
per-tire block, prefix tire_{fl,fr,rl,rr}_ (16 × 4)	longitudinal_slip, slip_angle_rad, camber_angle_rad, force_world_{x,y,z}_n, moment_world_{x,y,z}_nm, force_wheel_{fx,fy,fz}_n, spindle_omega_radps, wheel_vx_mps, slip_ratio, deflection_m

force_wheel_* and slip_ratio are derived from spindle state and the world-frame force so they are computed identically on rigid and CRM terrain (on CRM the tire force comes from the FSI solver, tire_force_source: crm_fsi). The paper's 15-D HMMWV state is vel_body_x_mps, vel_body_y_mps, roll_rad, pitch_rad, roll_rate_radps, ang_vel_body_y_radps, yaw_rate_radps

• tire_*_force_wheel_fz_n (4) + tire_*_spindle_omega_radps (4); action is the driver triple; pose for open-loop rollout scoring is pos_x_m, pos_y_m, yaw_rad. Recording starts after a settle/warm-up window (warmup_s 2.5 s rigid, 0.2 s CRM), so time_s does not start at 0.

Arm (arm, 47 columns). Each row is one 50 Hz control step written as a transition (s, a, s'): q_0..3, qd_0..3 (joint angle rad / rate rad/s), qcmd_0..3 (current joint command), act_0..3 (Δq_cmd), qcmd_next_0..3 (command applied over this step — the paper's action), q_next_0..3, qd_next_0..3, end-effector position in world (ee_{x,y,z}, ee_next_*) and in the vehicle base frame (ee_base_{x,y,z}, ee_next_base_*), plus collision (0/1), collision_kind (ground / track / joint_limit / empty), contact_force_n. Episodes start from the home pose with random command increments and terminate on the first contact or joint-limit hit, so lengths are 9–500 steps (mean ≈ 58). The paper's 8-D state is [q, qd] with the end effector recovered by forward kinematics.

Tracked (tracked, 42 columns). The HMMWV chassis block without body_slip_rad and without tire channels, plus left_sprocket_speed_radps, right_sprocket_speed_radps. The paper's 3-D state is vel_body_x_mps, vel_body_y_mps, yaw_rate_radps; action is the driver triple.

episodes.parquet and the metadata bundle

episodes.parquet flattens each episode's dataset_index.json entry and its JSON sidecar (nested values are JSON strings): episode_id, split, scenario_family, rows, duration_s, warmup_s, source_shard, parquet_file, and per dataset e.g. height_map_index / height_map / terminated_out_of_bounds (bumpy), terminated_near_boundary, crm_particles, crm_force_summary, full driver profile (CRM), collision_kind, collision_links, start_q (arm), diverged (tracked), tire_nominal_radius_m.

metadata.tar.gz is the untouched original metadata: per shard dataset_index.json and collector_config.resolved.json (every materialised scenario: driver profile, seed, family, terrain and solver settings), every per-episode sidecar, and the shard-plan manifests. It is what lets the release be turned back into the collectors' original directory tree (below).

Loading

Streaming with 🤗 datasets (no download of the 44 GB flat set required):

from datasets import load_dataset
ds = load_dataset("harryzhang1018/NeDM", "hmmwv_crm", split="val", streaming=True)
for row in ds.take(3):
    print(row["episode_id"], row["time_s"], row["vel_body_x_mps"], row["tire_fl_force_wheel_fz_n"])
episodes = load_dataset("harryzhang1018/NeDM", "hmmwv_bumpy_episodes", split="train")

Arrow / DuckDB — one shard at a time, with row-group statistics for pushdown:

import pyarrow.parquet as pq
t = pq.read_table("raw/hmmwv_flat/train/shard_017.parquet",
                  columns=["episode_id", "time_s", "vel_body_x_mps", "yaw_rate_radps"],
                  filters=[("scenario_family", "==", "chirp_steer")])

Reproducing the paper with the code repository (conda env create -f environment.nedm.yml):

# training caches -> artifacts/training_datasets/, then any config in configs/ runs verbatim
PYTHONPATH=src python scripts/release/download_nedm_datasets.py --dataset all --no-raw --processed
PYTHONPATH=src python scripts/training/train_hmmwv_dynamics.py --config configs/tracked_transformer_v1.json

# raw Parquet -> the collectors' original per-episode CSV tree under artifacts/datasets/,
# so scripts/preprocess/* and the RL reference builders run unchanged
PYTHONPATH=src python scripts/release/download_nedm_datasets.py --dataset arm --rehydrate

The rehydrated CSVs carry the float32 values the trainer uses; caches rebuilt from them are bit-identical to the ones in processed/ (this is checked in the release validation).

Processed caches

Cache	Trained model	State	Action	Transitions (train / val)	Size
hmmwv_tire_rigid_300g_normal_force_omega_seq_v1	terrain-conditioned HMMWV NN-ROM (flat share)	15-D	3-D	128,043,338 / 32,475,755	23.1 GB
hmmwv_crm_2000_normal_force_omega_seq_v1	terrain-conditioned HMMWV NN-ROM (CRM share)	15-D	3-D	2,280,431 / 602,530	0.4 GB
arm_dyn_v3_8d_seq16_v1	arm NN-ROM	8-D [q, q̇]	4-D q_cmd	763,886 / 141,754	87 MB
tracked_drive_v2_seq16_v1	tracked-base NN-ROM	3-D [vx, vy, r]	3-D	1,407,465 / 273,859	81 MB

Each cache holds contiguous float32 arrays {train,val}_{states,actions,targets,rollout}.npy (targets = states[t+1] − states[t], rollout = pose per recorded row), episode_starts / episode_lengths, {train,val}_episodes.json (episode ids and provenance) and metadata.json (state_fields, action_fields, dt_s, train-split mean/std used for normalisation). Values are raw physical units; the model applies the statistics.

Known limitations

• hmmwv_bumpy episodes are short (mean 3.3 k rows) because 78 % end on the 0.9 × 500 m keep-in guard; the regime is meant as a test set.
• The arm collection is restricted to free-space motion (episodes end at first contact) and under-samples the lower/rear workspace.
• CRM episodes are 12–18 s long (SPH cost) and use rigid-mesh tires; the CRM tire "force" is the fluid–solid interaction force.
• Simulation is deterministic and noise-free; there is no sensor model.

Citation

@article{zhang2026abstraction,
  title   = {Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control},
  author  = {Zhang, Harry and Negrut, Dan},
  journal = {Preprint},
  year    = {2026}
}

License: BSD-3-Clause (same as the code). Simulation assets are Project Chrono's HMMWV and M113 models; the LRV arm geometry is in the code repository (src/arm_model/).