README.md

metadata

license: cc-by-4.0
language:
  - en
tags:
  - neuroscience
  - reinforcement-learning
  - drosophila
  - simulation
pretty_name: FlyControl paper data

FlyControl data

Research records for A Drosophila connectome prior enables adaptable embodied control.

Code: FlyControl.

Figure	Experiment	Models
Figure 2	Fly terrain locomotion	Adult, MLP and ER; 20 seeds each
Figure 3	Fly turning control	Adult and MLP; 24 seeds each
Figure 4	Fly speed control	Adult and MLP; 20 seeds each
Figure 5b–d, S7	Ant flat ground and floor friction	Adult and MLP; 20 seeds each
Figure 5e	Ant U-Maze, Medium Maze and Large Maze	Seven controller families; 20 seeds per maze and family
Figure 5f–j	Ant Medium Maze trajectories and navigation	Adult and MLP; 20 seeds each
S3	Developmental direction control	Adult and Larva; 6 seeds each
S4	Developmental speed control	Adult and Larva; 6 seeds each
S5	BANC descending and motor readouts	6 seeds per readout; initial and final states
S6	BANC regional recovery	3 seeds per readout
S8	Ant Large Maze trajectories, speed and route complexity	Seven controller families; 20 seeds each
Figure 6a–e	Cassie balance; Tripod, Go1 and Hexapod walking	Connectome and MLP; 20 seeds per body and family
Figure 6f	Cassie standing and lateral-motion stability	Connectome and MLP; 20 seeds each
Figure 6g	Go1 tilt, pace and heading stability	Connectome and MLP; 20 seeds each
S9	Tripod direction and path stability	The Figure 6 Tripod policies
S10	Hexapod path and lateral-corridor stability	The Figure 6 Hexapod policies

The seven Ant maze controller families are Adult connectome, MLP, GRU, LSTM, Transformer, Reservoir and Reservoir+RO. The Ant flat-ground MLP uses two 1,378-unit ReLU hidden layers.

The archives contain episode records, trajectories, figure source tables, statistical summaries and experimental inputs. Corresponding policies are in FlyControl-models.

Figure 5, S7 and S8

archives/figure5.tar.gz contains:

• Ant flat ground: 4,000 evaluation episodes, motion endpoints and displacement trajectories from 40 policies.
• Floor friction: 4,000 episodes across four friction coefficients, with seed summaries for S7.
• Ant mazes: 42,000 evaluation episodes from 420 policies across U-Maze, Medium and Large mazes.
• Medium Maze: example trajectories, deadline curves, geodesic efficiency, route-turn groups and physical-failure records.
• Large Maze: seven example trajectories and speed records, route-stratified metrics and seed summaries for S8.
• Statistical source tables, including the 26- and 29-comparison families.
• Teacher-labelled training samples and an index mapping them to maze, controller, optimization seed and training stage.

Extracted files are under data/figure5/. Start with experiment_map.csv, model_index.json, maze/episodes.csv, source_data/, S7/, S8/ and training_labels_index.json. Identical training-label files are stored once and referenced by checksum.

Maze training labels were generated using the GoalAnt teacher from Farama-Antmaze-Dataset-Generation, checkpoint SHA-256 1b835cc5185756c5af8cd0720dae0e4895efcec48ee577c450d4a5a633fb953e.

Optimization seed identifies the policy replicate; reset seed identifies an evaluation episode. Trajectory distances are in metres, time in seconds and velocity in metres per second. Each source table retains its original field names and units.

Figure 6, S9 and S10

archives/figure6.tar.gz contains 22,400 evaluation episodes and their trajectories from 160 policies: four bodies, two controllers, 20 training seeds, seven conditions and 20 matched evaluation states per seed and condition.

• Figure 6a–e: Cassie balance and Tripod, Go1 and Hexapod walking; nominal trials, lateral pushes, reduced friction, 80-ms policy delay, 25% sensor loss and 60-s trials.
• Figure 6f: Cassie standing fraction, early lateral motion, reduced-friction standing duration and the matched snapshot trajectories.
• Figure 6g: Go1 tilt, pace, heading and the matched snapshot trajectories.
• S9: Tripod lateral displacement, heading error, late drift, outcome composition and success by condition.
• S10: Hexapod lateral displacement, late drift, corridor retention, first-exit times and success by condition.

Files are under data/figure6/. experiment_map.csv maps panels to source tables; model_index.json identifies the corresponding models. twenty_seed/episodes.csv contains the cohort, details/ and path_summary/ contain the main-figure source data, and comprehensive/ contains the shared statistical and time-series data for Figure 6, S9 and S10, including the 10-endpoint and 3,150-comparison statistical families. These three analysis folders are under twenty_seed/.

Raw records use raw/<body>/<method>_seed<training_seed>/<evaluation_seed>/<condition>.json and .npz. The NPZ trajectory array contains time, qpos, qvel, executed Gaussian action, forward velocity, torso tilt and applied lateral force. Each episode JSON supplies nq, nv and nu for the variable-width blocks. Time is in seconds, distances in metres, velocity in metres per second, tilt in degrees and force in newtons. experiment_protocol.json lists the bodies, commands, training budgets, seeds and condition durations.

To download this group, use manifests/figure6.jsonl and archives/figure6.tar.gz in the example below, then extract with --figure figure6.

Download and extract

from huggingface_hub import snapshot_download
data = snapshot_download(
    'UncleStrawberryBear/FlyControl-data', repo_type='dataset',
    allow_patterns=['README.md', 'archive_index.json', 'extract_data.py',
                    'manifests/figure5.jsonl', 'archives/figure5.tar.gz'])

python /path/to/data-snapshot/extract_data.py --figure figure5 --output ./paper-data

Use figure2, figure3, figure4, S3, S4, S5, S6 or figure6 to extract the other archives. archive_index.json lists archive sizes and checksums; manifests/ lists member paths, source paths and SHA-256 values. The extractor verifies these checksums. figure_map.csv maps paper figures to their archived records. Source and licensing information is in RIGHTS.md.

License

The authors' original research data, trajectories, figure source tables and accompanying documentation are released under Creative Commons Attribution 4.0 International (CC BY 4.0). Third-party materials retain their upstream licenses and attribution; see RIGHTS.md.

Python utilities in this data repository use the MIT License.