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adversarial-control

adversarial-control

AMP, ADD, ASE, and task-conditioned adversarial control workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: adversarial-control description: "AMP, ADD, ASE, and task-conditioned adversarial control workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

adversarial-control

Use this sub-skill for AMP, ADD, and ASE workflows in MimicKit, plus AMP task-conditioned location and steering routes. If the request is about SMP score-matching priors or prior-based task policies, hand off to the smp sub-skill instead.

What it covers

  • AMP imitation-only controllers and AMP task-mixed controllers.
  • ADD differential discriminator training and control.
  • ASE latent skill routing, encoder rewards, and diversity regularization.
  • Task-conditioned location and steering control built on the AMP family.
  • The task-dodgeball environment shape as a related reference point, with the full policy route handled elsewhere.

Start here

  1. Pick the route in references/amp-add-ase-workflows.md.
  2. Confirm the discriminator, reward, and latent knobs in references/discriminator-and-latent-configs.md.
  3. Use references/troubleshooting.md before changing configs or claiming a blocker.
  4. Keep unresolved simulator, asset, or data gaps explicit.

Route rules

  • AMP imitation-only: same motion-clip imitation path, no task reward mix.
  • AMP task-conditioned: combine motion imitation with location or steering tasks using the task AMP configs.
  • ADD: use the differential discriminator path; the discriminator learns on target-vs-agent observation differences.
  • ASE: use the latent-conditioned AMP path with encoder reward and diversity loss.
  • Task-dodgeball: use the task env as a reference point only; the full policy route belongs with the SMP sub-skill.

Do not route here

  • SMP score-matching priors and task-policy training around those priors.
  • DeepMimic, AWR, or LCP motion-imitation workflows.
  • Motion conversion and visualization.
  • Backend install, simulator selection, or low-level engine plumbing.

Evidence distilled into this skill

This sub-skill was distilled from the AMP, ADD, and ASE docs plus the matching env, agent, and config families in a MimicKit checkout. Use the bundled references in this subtree for operational details; do not send future agents back to source-doc reads for routine routing.

Stable config warning

If a change affects num_disc_obs_steps, disc_dof_vel_obs, or latent_dim, assume the saved model and the surrounding config pair may no longer match and re-check the whole route before reusing a checkpoint.

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