microduck-running

A robustified forward-running policy for the stock MicroDuck on flat ground. The default artifact is iteration 12,195, continued from the 1.687 m/s speed frontier with progressively stronger velocity pushes, centre-of-mass variation, and initial tilt.

Status: simulation-only hardware candidate; not validated on hardware. It retains 1.651 m/s mean body-forward speed in the nominal simulation battery and 1.612 m/s under the combined backlash-plus-disturbance battery. Heading and lateral drift remain substantial.

Normal simulation rollout — iteration 12,195

Open the full silent MP4.

Run-into-mat edit — legacy iteration 8,749

Open the full silent run-into-mat MP4.

The mat video uses the preserved iteration-8,749 controller, not the new root policy. Its obstacle, collision augmentation, camera treatment, post-impact motor cutoff, and sparks are scripted demonstration elements. The policy has no obstacle perception and did not learn to react to a mat. Both videos are silent.

Versions

Command

The 13-D command block is [twist(3), head_pose(4), body_pose(6)].

An exact-zero standing bucket made up 3% of training environments, so twist = [0, 0, 0] is the intended idle command. The speed objective rewards forward progress and can exceed the command; twist[0] is not a hard speed limiter.

Try it in simulation

The matching source is Vottivott/microduck-playground@828d950:

git clone https://github.com/Vottivott/microduck-playground.git
cd microduck-playground
git checkout 828d950134e29a8d04cbb51720a22c8729047fb7
uv sync

hf download HannesVonEssen/microduck-running policy.onnx config.json --local-dir policies/running
uv run scripts/infer_policy.py \
  --walking policies/running/policy.onnx \
  --new-cmd-obs \
  --lin-vel-x 2.2

Continue training

checkpoint.pt is the complete iteration-12,195 PPO state: actor, critic, optimizer, observation normalizers, and curriculum counter.

mkdir -p logs/rsl_rl/running/release-12195
cp checkpoint.pt logs/rsl_rl/running/release-12195/model_12195.pt

MICRODUCK_RUNNING_TARGET_MAX_SPEED=2.2 \
MICRODUCK_RUNNING_SPEED_CAP=2.4 \
MICRODUCK_RUNNING_HIGH_SPEED_STAGE_INTERVAL=750 \
MICRODUCK_RUNNING_ACTION_RATE_WEIGHT=-0.10 \
MICRODUCK_RUNNING_FORWARD_PROGRESS_WEIGHT=5.0 \
MICRODUCK_RUNNING_ROBUST_PUSH_MPS=0.10 \
MICRODUCK_RUNNING_ROBUST_TRUNK_COM_M=0.008 \
MICRODUCK_RUNNING_ROBUST_HEAD_COM_M=0.006 \
MICRODUCK_RUNNING_ROBUST_INITIAL_TILT_DEG=2.0 \
uv run train Mjlab-Running-Flat-MicroDuck \
  --agent.resume True --agent.load-run release-12195 \
  --agent.load-checkpoint model_12195.pt --agent.max-iterations 100

PyTorch checkpoints use pickle internally; load them only from a repository and revision you trust.

Contract

• input: obs, float32 [1, 61]
• output: actions, float32 [1, 14]
• control rate: 50 Hz
• action scale: 1.0, joint-position targets around MicroDuck HOME
• observation normalizer: baked into policy.onnx
• output clipping: none; training also used unclipped actor outputs
• entry pose: standing
• robot: MicroDuck hardware revision 1, 14 Dynamixel XL330 servos
• hardware modifications: none

Checkpoint-to-ONNX parity over zero and deterministic random observations had a maximum absolute action error of 7.63e-6. See eval/checkpoint-onnx-parity.json.

Simulation evaluation

All batteries used a 2.20 m/s command, 1-second warm-up, and 10-second measurement horizon.

Stress means ±0.10 m/s planar pushes every 3–6 seconds, ±10 mm trunk CoM, ±6 mm head CoM, and ±2° initial pitch/roll. The high-grip case widens foot friction from 0.7–1.3 to 0.7–1.8. Raw outputs are under eval/.

The candidate was trained with the ordinary robot model, then evaluated unchanged with the backlash model. A separately backlash-trained continuation performed worse and was rejected. Robustification costs about 2.1% nominal speed relative to iteration 11,748 but gives the stronger overall release.

Sim-to-real boundary

The policy retains substantial deployment-oriented modeling:

• BAM XL330 M6 voltage control, back-EMF, current limiting, 6.5–8.2 V battery variation, and load-dependent sag;
• 3–6 physics-step actuator delay, 0–1-step IMU delay, and one-control-step joint-velocity lag;
• foot friction randomized from 0.7–1.3;
• trunk mass/inertia ±5%, armature ±10%, BAM joint friction ±10%, encoder bias ±0.015 rad, IMU misalignment up to 6°, and actor-observation noise;
• in the final stage: ±0.10 m/s pushes, ±8 mm trunk CoM, ±6 mm head CoM, and ±2° initial pitch/roll.

Important remaining relaxations are flat terrain, ordinary non-backlash training, disabled head/body pose tracking, weaker posture and angular-motion regularization, and no motor-gain randomization. Backlash was tested, not part of the selected policy's training distribution. Transfer is plausible, not proven; heading drift, impact loading, real contact, servo thermal limits, and unmodeled compliance remain concerns.

Hardware safety

For a first robot test, use a support/catch rig, keep a remote torque-off path, start with low commands and ramp conservatively, and log servo current, temperature, voltage, fall state, and commanded/applied twist. Do not begin at 2.20 m/s. Stop on repeated saturation, overheating, unexpected oscillation, or systematic forward pitching.

Provenance and files

Training task Mjlab-Running-Flat-MicroDuck; PPO with rsl_rl; checkpoint iteration 12,195. Source: Vottivott/microduck-playground@828d950, based on pollen-robotics/microduck_rl@d424a0c. Exported with scripts/export.py, which bakes the observation normalizer into the ONNX graph.

• policy.onnx — default robust deployment policy
• checkpoint.pt — default resumable PPO checkpoint
• manifest.json — machine-readable contract, evaluation, and provenance
• eval/ — raw evaluation records and checkpoint/ONNX parity record
• media/preview.mp4 — robust policy's 10-second close rollout
• media/run-into-mat.mp4 — scripted legacy iteration-8,749 demonstration
• lineage/iteration-11748/ — speed-parent model, checkpoint, video, and evals
• legacy/iteration-8749/ — exact mat-video policy, checkpoint, and clean video
• SHA256SUMS — artifact integrity hashes

Format inspired by RemiFabre/microduck-flamingo-cycle.