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Aldarondo 2024 locomotion retargeted to Fetch
This dataset contains the strict locomotion subset of all 38 sessions from the
coltrane rat in Aldarondo et al. (2024), deterministically retargeted onto the
unmodified 10-DoF Brax v1 Fetch body. It is the data-only input to Demo F in the
Embodied SSL + RL workshop.
Each row is a non-overlapping 64-frame trajectory sampled at 50 Hz. Rows contain only the derived Fetch trajectory and source provenance; raw rat keypoints, neural recordings, behavior labels, and video are not redistributed.
Data fields
Each data/<split>/<session>.npz shard contains:
| field | shape | dtype | meaning |
|---|---|---|---|
joint_angles |
(N,64,10) |
float32 | Fetch joint angles, radians |
root_position |
(N,64,3) |
float32 | Fetch root trajectory, Fetch model units |
root_quaternion |
(N,64,4) |
float32 | scalar-first root quaternion |
feet_local |
(N,64,4,3) |
float32 | realized Fetch foot endpoints in root frame |
contacts |
(N,64,4) |
uint8 | source-derived stance mask |
command |
(N,3) |
float32 | hindsight forward/lateral displacement and yaw |
source_start |
(N,) |
int32 | first frame in the named source session |
source_speed_mps |
(N,) |
float32 | net rat displacement speed; may be small on curved paths |
source_path_speed_mps |
(N,) |
float32 | mean rat path speed |
ik_foot_rmse |
(N,) |
float32 | semantic endpoint fitting error |
contact_speed_mean |
(N,) |
float32 | realized Fetch stance-foot speed |
minimum_foot_height |
(N,) |
float32 | minimum realized foot height |
joint_limit_fraction |
(N,) |
float32 | fraction within 1% of a joint limit |
Foot ordering is front-right, front-left, back-right, back-left. Joint ordering
and every transformation parameter are recorded in manifest.json.
Construction
The builder applies Demo B's frozen geometric locomotion screen to non-overlapping 64-frame blocks: mean path speed above 0.10 m/s, coordinated joint motion, less than 90 degrees turning, and limited neck-height drift. Retargeting then uses a smoothed trunk frame and four semantic paws, body-size normalization, stance detection/pinning, and bounded sequence-level inverse kinematics with pose/velocity/acceleration regularization. Rodent bone rotations are not copied.
Train, validation, and test splits contain 26, 6, and 6 entire recording sessions. No session appears in more than one split.
Intended use and limitations
The dataset supports conditional motion modelling, motion-prior diagnostics, and simple control experiments on Fetch. It represents rodent-derived Fetch motion, not rat biomechanics: morphology, length, mass, actuation, and physical feasibility differ. The trajectories are kinematic IK results and are not torque supervision or guaranteed dynamically feasible demonstrations.
Source, attribution, and license
The source recordings are:
Aldarondo, D. et al. A virtual rodent predicts the structure of neural activity across behaviours. Nature 632, 594–602 (2024). https://doi.org/10.1038/s41586-024-07633-4
Original dataset: https://doi.org/10.7910/DVN/FB0MZT
The Harvard Dataverse source declares the Open Data Commons Attribution License (ODC-By) 1.0. This derived dataset is redistributed under ODC-By 1.0 and retains the required source attribution. Users should also cite the original paper and dataset. The Fetch implementation originates in Brax, licensed under Apache 2.0.
Reproduction
The exact schema, source hashes, code hashes, configuration, per-session counts,
quality gates, and shard SHA-256 values are in manifest.json. Builder and
validator source live in the companion workshop repository under
demo_f/dataset/.
Release statistics
Schema 1.0.0 contains 2,903 clips: 2,156 train, 335 validation, and 412 test. Generated statistics are authoritative in manifest.json.
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