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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+run.mp4 filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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LICENSE-HARDWARE

@@ -0,0 +1,12 @@
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
+SPDX-License-Identifier: CC-BY-NC-SA-4.0
+
+The 3D hardware design files in this repository are licensed under the
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
+license.
+
+You may read the complete legal code at:
+https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.en
+
+A human-readable summary is available at:
+https://creativecommons.org/licenses/by-nc-sa/4.0/

NOTICE

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+MicroDuck Playground
+Copyright 2026 Hannes von Essen / Vottivott
+
+MicroDuck RL is an independent local snapshot refactor of
+Vottivott/microduck-playground, derived from pollen-robotics/microduck_rl.
+This snapshot starts fresh local Git history rather than retaining the source
+repository history. Original copyright and license notices remain applicable.
+See docs/PROVENANCE.md for source snapshot details.
+
+The upstream project and MicroDuck robot assets are copyright their respective
+contributors and are used under their published licenses.
+
+The swing-seat generator, retention components, and experiment-specific
+training/evaluation additions were developed for the original MicroDuck Playground repository.
+
+Software: Apache License 2.0.
+3D hardware design files: Creative Commons Attribution-NonCommercial-
+ShareAlike 4.0 International (CC BY-NC-SA 4.0), following the upstream
+repository's hardware-file licensing convention. See LICENSE-HARDWARE.

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- reinforcement-learning
+- robotics
+- mujoco
+- microduck
+- ppo
+- onnx
+model-index:
+- name: MicroDuck Running
+  results: []
+---
+
+# MicroDuck Running
+
+A PPO locomotion policy for the 14-actuator MicroDuck biped, trained in MuJoCo Warp through mjlab. The final policy achieves **1.50 m/s mean body-forward speed** in simulation at a 2.0 m/s command. **The sustained 2.0 m/s goal was not met.**
+
+This release includes the normalized ONNX policy, the final training checkpoint, a simulation video, and evaluation measurements. It is a simulation research checkpoint; hardware performance has not been evaluated for this release.
+
+## Watch the policy
+
+<video controls src="run.mp4" width="720"></video>
+
+[Download the 10-second replay](run.mp4). The replay uses the final checkpoint, seed 123, one simulated robot, and a 2.0 m/s forward command. A single replay does not establish population-level reliability; see the evaluations below.
+
+## Measured results
+
+All results below use a 2.0 m/s forward command with zero commanded lateral and yaw velocity. Each evaluation uses 512 parallel environments and excludes a one-second warm-up from speed measurements.
+
+| Metric | Three 10-second evaluations | 30-second evaluation |
+|---|---:|---:|
+| Mean body-forward speed | 1.499 m/s | 1.499 m/s |
+| Mean straight-progress speed | 1.234 m/s | See raw evaluation |
+| Survival | 96.74% | 91.21% |
+
+The short evaluations use seeds 123, 456, and 789; the long evaluation uses seed 456. These are simulation measurements, not hardware results or instantaneous peak speeds. Survival uses the evaluator's 70-degree fall-tilt threshold; it does not establish safe hardware operation.
+
+The policy drifts in heading. Body-forward speed measures motion along the robot's changing forward axis; straight progress measures motion along its initial heading and penalizes failed trajectories. This difference matters when comparing running speed with useful straight-line travel. The evaluation uses `VelocityCommandCommandOnly`, without an external heading-hold controller.
+
+The acceptance criterion was at least 2.0 m/s mean body-forward speed with at least 95% survival across the short evaluations, plus at least 2.0 m/s and 90% survival in the long evaluation. **`target_met` is false.** Raw measurements are in [evaluation/](evaluation/), including [the aggregate result](evaluation/result.json).
+
+## Release contents
+
+| File | Purpose |
+|---|---|
+| `policy.onnx` | Inference policy exported with observation normalization baked in |
+| `model_10998.pt` | Final PPO checkpoint; requires the matching training code |
+| `run.mp4` | Ten-second simulation replay |
+| `evaluation/*.json` | Per-seed, long-rollout, and aggregate measurements |
+| `SHA256SUMS.txt` | Release file integrity hashes |
+| `LICENSE`, `NOTICE`, `LICENSE-HARDWARE` | License and attribution notices |
+
+## Inference interface
+
+- Control frequency: **50 Hz**.
+- Actor observation: **61 values**, comprising 48 proprioceptive values and a 13-value command block: twist (3), head pose (4), body pose (6).
+- Action output: **14 values**. These require the matching runtime's action scaling, joint order, and actuator interpretation; they are not direct motor commands.
+- The ONNX export includes observation normalization. Do not normalize observations a second time.
+- Preserve the matching observation definitions, command padding, joint layout, and actuator model. This policy was trained without action low-pass filtering.
+
+With `onnxruntime` and NumPy installed, inspect the interface using:
+
+```python
+import onnxruntime as ort
+
+session = ort.InferenceSession("policy.onnx", providers=["CPUExecutionProvider"])
+print([(x.name, x.shape, x.type) for x in session.get_inputs()])
+print([(x.name, x.shape, x.type) for x in session.get_outputs()])
+# obs must contain correctly constructed, unnormalized runtime observations.
+# actions = session.run(None, {session.get_inputs()[0].name: obs})[0]
+```
+
+The checkpoint is not a standalone robot controller. The matching local training package is `mjlab-microduck`, task `Mjlab-Running-Flat-MicroDuck`. A public URL for this refactored code has not yet been supplied. Upstream code provides context but is not asserted to reproduce this release exactly.
+
+## Training
+
+This policy was trained locally using PPO/rsl_rl, mjlab 1.3.0, MuJoCo Warp, Python 3.12, PyTorch 2.9.1, and an NVIDIA RTX 3070. The continuation used 512 environments, 24 steps per environment per update, and seed 42.
+
+Training resumed the early `model_999.pt` policy and performed **10,000 additional PPO updates**, producing `model_10998.pt`. A gradual curriculum raised the maximum forward command to 2.0 m/s; the reward speed cap was 2.2 m/s. The training source commit was `104b1d194b82320bcb4a5df42b6d2b88f2094306` in the local refactored repository. This release selects the final checkpoint, not a best checkpoint found through a full checkpoint sweep.
+
+## Limitations
+
+- The achieved mean speed is below the 2.0 m/s training target.
+- Heading drift reduces straight-line progress substantially.
+- About 8.8% of environments failed the survival criterion over 30 seconds.
+- Evaluation covers flat simulated terrain and the specified seeds and commands, not arbitrary terrain, disturbances, or hardware.
+- Hardware transfer and matching-runtime validation remain outstanding.
+
+## Attribution and licensing
+
+The training code is an independent refactor of [Vottivott/microduck-playground](https://github.com/Vottivott/microduck-playground), based on source commit `828d950134e29a8d04cbb51720a22c8729047fb7` plus local changes, and derives from [pollen-robotics/microduck_rl](https://github.com/pollen-robotics/microduck_rl). Original contributions and copyright remain with their authors, including Hannes von Essen / Vottivott and the MicroDuck contributors.
+
+The software license is Apache-2.0; see `LICENSE` and `NOTICE`. Upstream 3D hardware designs have a separate CC BY-NC-SA 4.0 license documented in `LICENSE-HARDWARE`. No hardware design assets are included in this model release.

SHA256SUMS.txt

@@ -0,0 +1,12 @@
+6bbd6dc4b93e1738f46ff4f341726e47f00e869c8f2ec166649caa13244b80cb  evaluation/final-long.json
+2868b2827448d4834ecc8dbcbd829e962c8c30c41df2d986ccbfcfa8a0f3474f  evaluation/final-seed123.json
+652e44c1de36b766f75575ab7d279c792edb5ff1d390acd69a14c32e362331a9  evaluation/final-seed456.json
+1c5f422d42505d837c8a8835ca8e918345283589fa2cf55189317ba459c49a9e  evaluation/final-seed789.json
+ddf7fa7de09ce783acacebb330b751e36920e6e82f1b4ddb436f0c516b82b918  evaluation/result.json
+2d3fd9ffffa70a4307498ec630b14a6b4731e010f8ddda92686eaf65733c2dfe  LICENSE
+06e399b1e287641708114bc13529a9c93261a3c359085f776ffd419812b5f936  LICENSE-HARDWARE
+b7821ff51c014b2b31f0fb0d1871cb93c05811adfcbc2661cfec5db9b0b5951b  model_10998.pt
+fa7e9d24f07049b1c66beb0460ca162d066e7f2b083e66ae3e953da6dd4d9c4f  NOTICE
+0281e6886b487a5df9760521a1c45a210b05113da03f3f3e5c894edc810d361a  policy.onnx
+47c790d4bf974e4edd7a4aeadcfeea8214b90970074f08d663c687a636c75244  README.md
+f9df85959677cd12f3db4234dbc3d2ee6403cd56519fbccaede1eb9c59d30ae7  run.mp4

evaluation/final-long.json

@@ -0,0 +1,44 @@
+{
+  "absolute_lateral_displacement_m": {
+    "mean": 18.526641845703125,
+    "median": 19.817947387695312,
+    "p90": 32.625553131103516
+  },
+  "actor_observation_dim": 61,
+  "checkpoint": "model_10998.pt",
+  "command_speed_mps": 2.0,
+  "duration_s": 30.0,
+  "final_absolute_heading_error_deg": {
+    "mean": 87.38008537283385,
+    "median": 84.69948884060314,
+    "p90": 160.09187505020174
+  },
+  "flight_events_per_second": 7.028835773468018,
+  "flight_fraction": 0.6389064788818359,
+  "forward_speed_mps": {
+    "clean_survivor_mean": 1.497441291809082,
+    "mean": 1.4990968704223633,
+    "median": 1.5237702131271362,
+    "p10": 1.3423871994018555,
+    "p90": 1.619022250175476
+  },
+  "heading_controller": "VelocityCommandCommandOnly",
+  "initial_heading_displacement_speed_mps": {
+    "mean": 0.6058936715126038,
+    "median": 0.6276978254318237,
+    "p10": -0.2204008847475052
+  },
+  "max_tilt_deg_median": 19.318693171118948,
+  "mean_absolute_heading_error_deg": 56.799149628341524,
+  "mean_absolute_lateral_speed_mps": 0.10739298909902573,
+  "mean_absolute_yaw_rate_radps": 1.4589488506317139,
+  "num_envs": 512,
+  "seed": 456,
+  "straight_progress_speed_mps": {
+    "mean": 0.6023264527320862,
+    "median": 0.6251664757728577,
+    "p10": -0.2536446452140808
+  },
+  "survival_fraction": 0.912109375,
+  "task_id": "Mjlab-Running-Flat-MicroDuck"
+}

evaluation/final-seed123.json

@@ -0,0 +1,44 @@
+{
+  "absolute_lateral_displacement_m": {
+    "mean": 4.7902607917785645,
+    "median": 4.361053466796875,
+    "p90": 9.242082595825195
+  },
+  "actor_observation_dim": 61,
+  "checkpoint": "model_10998.pt",
+  "command_speed_mps": 2.0,
+  "duration_s": 10.0,
+  "final_absolute_heading_error_deg": {
+    "mean": 50.147139603206895,
+    "median": 43.680541893281436,
+    "p90": 102.89865765769633
+  },
+  "flight_events_per_second": 7.060634136199951,
+  "flight_fraction": 0.6389680504798889,
+  "forward_speed_mps": {
+    "clean_survivor_mean": 1.498811960220337,
+    "mean": 1.4987027645111084,
+    "median": 1.5198016166687012,
+    "p10": 1.3502960205078125,
+    "p90": 1.6200671195983887
+  },
+  "heading_controller": "VelocityCommandCommandOnly",
+  "initial_heading_displacement_speed_mps": {
+    "mean": 1.1756470203399658,
+    "median": 1.274877905845642,
+    "p10": 0.7284356951713562
+  },
+  "max_tilt_deg_median": 17.23110732300708,
+  "mean_absolute_heading_error_deg": 26.70739203220552,
+  "mean_absolute_lateral_speed_mps": 0.10519511997699738,
+  "mean_absolute_yaw_rate_radps": 1.4563381671905518,
+  "num_envs": 512,
+  "seed": 123,
+  "straight_progress_speed_mps": {
+    "mean": 1.2285314798355103,
+    "median": 1.3408124446868896,
+    "p10": 0.7291788458824158
+  },
+  "survival_fraction": 0.966796875,
+  "task_id": "Mjlab-Running-Flat-MicroDuck"
+}

evaluation/final-seed456.json

@@ -0,0 +1,44 @@
+{
+  "absolute_lateral_displacement_m": {
+    "mean": 4.850092887878418,
+    "median": 4.641942501068115,
+    "p90": 9.151276588439941
+  },
+  "actor_observation_dim": 61,
+  "checkpoint": "model_10998.pt",
+  "command_speed_mps": 2.0,
+  "duration_s": 10.0,
+  "final_absolute_heading_error_deg": {
+    "mean": 48.762564635796934,
+    "median": 41.127632332489526,
+    "p90": 98.61620418017921
+  },
+  "flight_events_per_second": 7.028905868530273,
+  "flight_fraction": 0.6373339891433716,
+  "forward_speed_mps": {
+    "clean_survivor_mean": 1.4947463274002075,
+    "mean": 1.4943912029266357,
+    "median": 1.5171191692352295,
+    "p10": 1.3179749250411987,
+    "p90": 1.6167409420013428
+  },
+  "heading_controller": "VelocityCommandCommandOnly",
+  "initial_heading_displacement_speed_mps": {
+    "mean": 1.17378830909729,
+    "median": 1.2443361282348633,
+    "p10": 0.7380818724632263
+  },
+  "max_tilt_deg_median": 17.19205400888024,
+  "mean_absolute_heading_error_deg": 26.77567684843272,
+  "mean_absolute_lateral_speed_mps": 0.10666482895612717,
+  "mean_absolute_yaw_rate_radps": 1.453669786453247,
+  "num_envs": 512,
+  "seed": 456,
+  "straight_progress_speed_mps": {
+    "mean": 1.2263376712799072,
+    "median": 1.3074625730514526,
+    "p10": 0.7286775708198547
+  },
+  "survival_fraction": 0.966796875,
+  "task_id": "Mjlab-Running-Flat-MicroDuck"
+}

evaluation/final-seed789.json

@@ -0,0 +1,44 @@
+{
+  "absolute_lateral_displacement_m": {
+    "mean": 4.681394577026367,
+    "median": 4.352655410766602,
+    "p90": 9.00128173828125
+  },
+  "actor_observation_dim": 61,
+  "checkpoint": "model_10998.pt",
+  "command_speed_mps": 2.0,
+  "duration_s": 10.0,
+  "final_absolute_heading_error_deg": {
+    "mean": 47.76244052619948,
+    "median": 39.6086631478758,
+    "p90": 102.50868800703446
+  },
+  "flight_events_per_second": 7.076333045959473,
+  "flight_fraction": 0.6413412094116211,
+  "forward_speed_mps": {
+    "clean_survivor_mean": 1.5066399574279785,
+    "mean": 1.5052788257598877,
+    "median": 1.5286027193069458,
+    "p10": 1.3462433815002441,
+    "p90": 1.6294121742248535
+  },
+  "heading_controller": "VelocityCommandCommandOnly",
+  "initial_heading_displacement_speed_mps": {
+    "mean": 1.1924302577972412,
+    "median": 1.284983515739441,
+    "p10": 0.746199369430542
+  },
+  "max_tilt_deg_median": 17.152648940011144,
+  "mean_absolute_heading_error_deg": 25.942588430082512,
+  "mean_absolute_lateral_speed_mps": 0.10692678391933441,
+  "mean_absolute_yaw_rate_radps": 1.4713090658187866,
+  "num_envs": 512,
+  "seed": 789,
+  "straight_progress_speed_mps": {
+    "mean": 1.2472805976867676,
+    "median": 1.3480452299118042,
+    "p10": 0.7459363341331482
+  },
+  "survival_fraction": 0.96875,
+  "task_id": "Mjlab-Running-Flat-MicroDuck"
+}

evaluation/result.json

@@ -0,0 +1,10 @@
+{
+  "checkpoint": "model_10998.pt",
+  "mean_body_forward_speed_mps": 1.499457597732544,
+  "mean_survival_fraction": 0.9674479166666666,
+  "mean_straight_progress_speed_mps": 1.234049916267395,
+  "long_rollout_speed_mps": 1.4990968704223633,
+  "long_rollout_survival_fraction": 0.912109375,
+  "target_met": false,
+  "criterion": "Mean body-forward speed >=2.0 m/s with >=95% survival across three 10-second seeds, plus >=2.0 m/s and >=90% survival in a 30-second rollout. Straight-line speed is reported separately."
+}

model_10998.pt

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policy.onnx

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run.mp4

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