Continue the released parkour policies

Both HF repos contain original full PPO checkpoints: actor, critic, observation normalizers, populated Adam state and environment step counter. Use the source revision in each repo's training/source.json. Install with uv sync --locked on Linux/CUDA. MuJoCo also needs system EGL or OSMesa libraries; the validation uses libosmesa6 and MUJOCO_GL=osmesa PYOPENGL_PLATFORM=osmesa. Only load trusted checkpoints.

Profile	HF repo	Stored iteration
`long-jump`	`HannesVonEssen/microduck-long-jump`	15,250
`backflip`	`HannesVonEssen/microduck-backflip`	12,000

hf download HannesVonEssen/microduck-long-jump checkpoint.pt --local-dir policies/long-jump
hf download HannesVonEssen/microduck-backflip checkpoint.pt --local-dir policies/backflip

# From the source root, without inherited MICRODUCK_* environment settings:
uv run python scripts/resume_release.py \
  --recipe experiments/parkour/resume.json --profile long-jump \
  --checkpoint policies/long-jump/checkpoint.pt \
  --output logs/long-jump-smoke --num-envs 64 --iterations 5
uv run python scripts/resume_release.py \
  --recipe experiments/parkour/resume.json --profile backflip \
  --checkpoint policies/backflip/checkpoint.pt \
  --output logs/backflip-smoke --num-envs 64 --iterations 5

After the smoke passes and its logs are inspected, choose a new output directory and e.g. --num-envs 4096 --iterations 250. Iterations are additional PPO updates, not a total target. For a subsequent continued.pt, add --descendant while keeping the same task/source/recipe. This explicitly relaxes the released-checkpoint hash guard; it does not infer another task's observation semantics. Output directories must be new. Original release assets are never replaced by the smoke or your continuation.

What is verified and saved

scripts/resume_release.py compares a pre-training snapshot against the input checkpoint's actor, critic, normalizers, Adam, iteration and counter. It applies curricula at the restored counter before collecting data, synchronizes the adaptive LR to restored Adam, and checks every step's observations, actions, rewards and physical state for finiteness. It saves resolved config, effective event parameters and reward weights, resume_initial.pt, continued.pt, TensorBoard logs and resume-validation.json. The mandatory normalized scripts/export.py export is checked against live, zero and random observations.

All recipes use procedural reverse-curriculum resets; no external motion/reset bank, private local path or unshipped teacher is required to continue these checkpoints. PPO state restoration is not restoration of old simulator, RNG, actuator buffers or per-environment state. A smoke pass is not a behavior, performance, robustness or hardware-safety result.

Training settings are not render profiles

Long jump: p1 launcher and recorded YAML were recovered. Entropy is 0.005; gap/drop are not pinned to the preview. The current source's range curriculum resumes at the saved counter (late range: gap 0.15–0.40 m, drop 0.15–0.35 m). This source includes later research changes, so it is a supported continuation rather than an exact reconstruction of p1's original implementation.
Backflip: f21 launcher and recorded YAML were recovered: 0.7–0.9 m drop, entropy0.003, takeoff weight45, minimum takeoff vz0, curriculum shift3000. The initial midflip probability0.25 becomes 0.15 at the restored mature stage. The preview's fixed0.8m profile is not the training recipe. Current published source remains the implementation boundary, not a promise of exact historical replay.

Recipe provenance/limitations are machine-readable in resume.json; recovered run YAML and hashes accompany the applicable HF training packages. Changes to curricula/rewards beyond these recipes are new experiments and should be logged. The training source's contact budgets include publication fixes.

Simulation only; not yet validated on hardware. For the backflip: ⚠️ Landings may break the robot. After continuation, evaluate complete physical maneuvers and resets with the correct command slots and action bounds, not reward totals alone. The original montage's exact checkpoint/seed mapping remains unverified; training documentation does not change that limitation.

Pinned public source: microduck-playground@b6aed572.