14 Robot Skills in 5 Days on a Single GPU: Queued RL Training for a Bipedal Robot Duck

Project Overview

MicroDuck is a bipedal robot duck: 15 XL330 servos, a Radxa ZERO 3W compute board, and a 50Hz control loop. The goal: train a full multi-skill policy library — walking, skating, standing up, rolling, kicking, picking up objects, sitting/standing — ending with 14/14 skills trained and exported as ONNX policies for deployment.

The stack:

• Simulation: mjlab (MuJoCo with the Warp GPU-parallel backend)
• Algorithm: PPO (rsl_rl), 4096 envs on flat terrain, reduced to 2048 on rough terrain
• Hardware: a single GPU, serial task queue, ~5 days total wall clock

Skill Matrix and Results

All skills in one animated grid:

Success Evaluation: Rollout GIFs of Every Skill

A good loss curve does not mean a good gait — rollout GIFs are the best acceptance test. Below are single-env rollouts of all 14 skills (300 frames each, converted with ffmpeg palettegen/paletteuse).

Locomotion family

Velocity-Flat (standard walking):

Velocity-Rough (rough-terrain walking; passed after OOM downgrade to 2048 envs):

Velocity-Rollers (skating with wheels on the feet):

Velocity-Swizzle:

Recovery family

VelStand-Flat (walking + fall recovery, one policy):

VelStand-Rough:

StandUp-Flat (stands up on the first attempt):

StandUp-Rough:

Trick family

Roulade (rolling; the "textbook" run-2 reward rewrite):

SitStand-Flat (sit↔stand switching with posture-conditioned moving targets):

SitStand-Rough (passed after the NaN-safe patch):

Task family

BallKick-Flat (blind kick — the actor can't see the ball, the critic can):

GroundPick-Flat (picking up objects via task-space definition instead of pose engineering):

Engineering: Three Tricks for Long Single-GPU Training Runs

1. Serial queue + .done markers

Task lists defined in bash associative arrays, executed strictly serially to avoid GPU contention. Completed tasks get a .done marker file; failures (rc≠0) get no marker and flow into the retry queue. The simplest reliable scheme for unattended single-GPU training.

2. OOM downgrade-and-retry

The first round of rough-terrain tasks died en masse within seconds — 4096 envs with rough terrain blew up VRAM. The fix was blunt: drop all rough tasks to 2048 envs and rerun. All passed.

Lesson: rougher terrain → more observation/physics state per env → budget your env count. Rule of thumb: ~70% envs for rough terrain.

3. OOM and NaN are two different failure classes

• OOM: dies within tens of seconds with rc=1; read the tail of the log
• NaN: dies after minutes-to-hours with ValueError: The observation group 'critic' contains NaN values; you need the wandb curves to locate the divergence source

The general NaN fix is a NaN-safe patch: NaN guards on reward/advantage + a nan_state termination flag (monitored as Episode_Termination/nan_state in wandb). Both SitStand-Rough and GroundPick-Rough passed with it.

Reward Design: An Economics Problem Per Skill

The most interesting part of the project — nine completely different reward-design philosophies behind 14 skills.

General principles

• Task reward magnitude ≈ 10, so shared regularization terms act with the same relative strength across skills
• DR/noise/regularization recipes are inherited from the velocity baseline; each skill only adds its task layer — port proven recipes, don't reinvent
• Goal terms give gradient; regularizers only collect tax: in large-motion skills, motion-damping terms (body_ang_vel, impact penalties) are crushed to near zero during exploration and introduced later via curriculum — taxing attempts kills discovery outright
• Potential-based progress rewards: pay only for increments past "furthest progress so far"; standing still earns nothing — inherently farming-proof

A few favorite cases

Velocity: deliberately harsh upright. At weight 1.0, a 4° forward lean costs only ~0.05 per step — effectively free — and the converged gait leaned 2–4° forward, with 2/3 of pushes tipping it forward. Tightened to weight 2.0 while leaving margin for transient leans. Also, spin-in-place commands got their own sampling bucket: 15% of envs get lin=0 + |ang|∈0.4,1.0.

StandUp: escape-basin design. Three-layer height reward: dense (broad gradient) + sharp (a 0.36→1.0 jump in the last centimeter) + L1 (breaks the static basin: sitting still is net negative). The real unlock was mid-roll spawn reverse curriculum — starting episodes mid-flip splits an unlearnable whole into a proven-learnable half.

Roulade: run-1 learned violent breakdance. Rewarding "accumulate 2π as fast as possible" with no contact constraint — the policy was genuinely optimal under that reward. Run-2: support gating (only ground-contact rotation counts) + landing reward gated on roll frontier ≥260° + progress pay rate capped at 3 rad/s + overspeed penalties. Style is the scarce resource.

BallKick: asymmetric actor-critic. No ball perception on the real robot (the operator aims), so the actor is ball-blind with robustness from ±2cm ball-placement domain randomization; but the critic sees the ball — value front-runs the kick payoff and helps the actor learn.

GroundPick: don't define the DOWN pose. Instead, three balanced forces — mouth-near-ground attraction + strong contact prohibition + vertical orientation — yield a mouth hovering at ground level. Task-space definition over pose engineering.

VelStand: three audit iterations. Recovery layer gated on "actually fell" (contributes exactly zero during clean walking); a unified, reachable "recovered" definition (tilt<25° and z>0.09); potential-based Δz progress for the last mile. Every round was a fight with reward-economics loopholes.

PPO Parameter Cheat Sheet

The core stabilizer is adaptive LR + desired_kl=0.01. No RNN, no CNN — plain MLP with obs normalization, entirely sufficient for 50Hz servo control.

Key Lessons

1. Queue + .done markers + downgrade-retry is the simplest reliable scheme for unattended single-GPU training
2. OOM and NaN are different failure classes: OOM dies fast, check the log tail; NaN dies young, trace the divergence in wandb curves
3. Rollout GIFs are the best acceptance test — a pretty loss curve is not a pretty gait
4. VRAM budgeting: ~70% envs for rough-terrain tasks is the empirical starting point
5. Reward design is economics: for every term, ask "will this tax kill the exploration you're trying to encourage?"

Training code is based on pollen-robotics/microduck_rl, simulation via mjlab (MuJoCo Warp), training framework rsl_rl. Happy to chat 🐾