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name: actuator-network description: "Validate Go1 deployment logs, build six-feature per-joint actuator samples, evaluate the small actuator MLP on CPU, and optionally train and export a bounded TorchScript model with safe diagnostics." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Actuator Network

Use this sub-skill for the offline actuator-model workflow: inspect a hardware_closed_loop deployment log, turn its 12-joint state history into six-feature samples, evaluate the supplied TorchScript actuator model on CPU, or deliberately opt in to a bounded CUDA training/export run.

Route and safety boundary

1. Read data format before interpreting a log or constructing samples.
2. Use workflow to choose CPU evaluation, data-only extraction, or explicitly approved training/export.
3. Consult API reference for the source helper signatures and troubleshooting for incomplete logs, shape errors, paths, CUDA, and headless operation.
4. Run inspect_actuator_log.py first. It is read-only and validates a pickle log or portable JSON fixture without importing Torch, training, networking, or plotting.
5. Run prepare_actuator_data.py to emit a deterministic JSON dataset to stdout or to a new explicit output path. It does not train, load a network, plot, or overwrite an existing output by default.

Robot data collection, LCM channels, calibration, controller launch, and physical safety belong to robot-deployment, not this sub-skill. Do not start a robot, publish LCM messages, SSH, transfer artifacts, or run the repository's source-relative scripts as a prerequisite.

Operating contract

• A valid source log has the shape {"hardware_closed_loop": [config, infos]} where infos is a chronological list of records. A portable JSON fixture may instead use {"records": [...]} or be a list of records; see data format.
• Every record must provide 12 finite values for joint_pos, joint_pos_target, joint_vel, tau_est, and torques. The first three may be a singleton (1, 12) row as produced by the deployment logger; torque arrays may be (12,) or a singleton row.
• The extractor computes error = joint_pos - joint_pos_target and uses [error[t], error[t-1], error[t-2], joint_vel[t], joint_vel[t-1], joint_vel[t-2]] as six inputs for each joint. The target is tau_est[t].
• The supplied artifact resources/actuator_nets/unitree_go1.pt is a TorchScript model contract, not a file to copy into this skill. Keep model weights and generated logs outside the skill tree.
• CPU model loading and a small forward pass are safe default checks. CUDA training and export are optional, explicitly bounded, and never run implicitly. Full training is not run by this skill.

Bundled files

• data-format.md — log envelope, five required fields, 12-joint shape rules, history alignment, target, and split.
• workflow.md — no-plot extraction, CPU evaluation, optional CUDA training/export, architecture, and artifact handling.
• api-reference.md — verified source helper signatures/defaults and semantics.
• troubleshooting.md — actionable diagnosis for missing/incomplete data, tau_est, short history, shape/path issues, CUDA, overwrite, and headless plotting.
• inspect_actuator_log.py — read-only pickle/JSON validator with machine-readable summary and non-zero failures.
• prepare_actuator_data.py — deterministic six-feature/one-target JSON extractor with stdout or explicit safe output.

The scripts intentionally contain no source-repository-relative imports. They are data checks and preparation helpers, not replacements for robot collection or a training launcher.

Verification boundary

The repository evidence was inspected from scripts/actuator_net/utils.py, train.py, eval.py, go1_gym_deploy/utils/logger.py, the example log format, README.md, and the existing TorchScript artifact contract. The example artifact was loaded on CPU and accepted (batch, 6) input with (batch, 1) output. The large example pickle was inspected for its envelope and record shapes. Full actuator training, interactive plotting, CUDA training, and physical deployment are not run or claimed here.