| # Sprint: from a licensed actor to 1.084 s |
|
|
| Our best selected sprint policy completes2 m in a **1.084 s five-seed mean,5/5 finishes**. |
| It is D1 launchboost checkpoint 900. The official published entry remains a different |
| checkpoint,2Brace-adapt750 at1.096 s. The0.988 s target was not reached. |
|
|
| ## Exact selected result |
|
|
| | Seed | Timed finish | |
| |---|---:| |
| |1|1.08 s| |
| |2|1.06 s| |
| |3|1.10 s| |
| |4|1.10 s| |
| |5|1.08 s| |
|
|
| ONNX SHA256: |
| `25bfd91009f56fcff2fdf8326a56995cba3737759e0e1d681f71ed2c639c9ce4`. |
| Training commit:`ad8c51e604cd73ceb7c58557905d640c0ee0a419`. |
| Local evaluation run:`01M4T4N650MJSRNHEZW8M6`. |
| Full records are in [best-score.json](../records/sprint-2m/best-score.json). |
|
|
| The featured clip shows seed 5's1.08 s finish, not the fastest seed 2. Its title reports |
| the1.084 s mean separately. The timed mean implies an effective2/1.084≈1.845 m/s; that |
| is a course-average measure, not the instantaneous peak speed or a hardware claim. |
|
|
| ## Baselines and the zero-training attempt |
|
|
| | Policy | Mean | Finishes | How it was used | |
| |---|---:|---:|---| |
| |Pollen sprint reference|4.892 s|5/5|Benchmark only| |
| |Hannes running|1.192 s|5/5|Apache-2.0 warm-start actor| |
| |Selected D1|1.084 s|5/5|Our adapted policy| |
|
|
| The improvement over the licensed parent is about 9.1%. Hannes' gait already accelerated |
| far beyond the Arena's maximum commanded speed, so we first tried a graph-level command |
| override instead of training. The wrapper writes rawvx before the actor's existing |
| normalizer and retains input/output names,61 observations and14 actions. We tested |
| 1.2,1.6,2.0,2.2,2.4,2.6 and forced lateral/yaw commands to zero. There was no useful speed |
| gain; we retained rawvx2.6 for consistency during training and export. |
|
|
| The wrapper is `scripts/override_command.py`; the sweep is |
| `scripts/sweep_command_override.py` in the experiment overlay. This is not input |
| normalizer replacement: the original mean and denominator remain intact. |
|
|
| ## Race environment |
|
|
| The course is2 m long,0.8 m wide, with0.15 m walls. The robot starts from STAND with±0.01 rad |
| joint noise and±2°yaw. The policy runs for0.5 s before the timer starts. Finish progress |
| is trunk world-x movement from the settle-end origin. Tilt beyond60°held0.3 s falls; |
| fall/timeout scores50 s. Nominal BAM uses7.35 V/drop0.1,3–6substep command delay and a |
| one-control-step joint-velocity observation lag. |
|
|
| Our3 s episodes include the run-up. Actor observations stay61D; the critic receives |
| privileged position/progress, true velocity, time and tilt-hold. We removed pushes, |
| wide mass/friction/voltage randomization and strong sim2real/style terms. Core rewards: |
| uncapped forward trunk velocity, finish bonus, large fall penalty, light yaw/lateral |
| cost and weak action-rate cost. Strong pose/slip/air-time/clearance shaping was removed. |
|
|
| Later code repairs the fall hold so settle steps do not count. The selected D1policy |
| predates that repair; selected historical overlays and current aligned overlays are |
| both provided. Do not confuse current source with the code that originally made a checkpoint. |
|
|
| ## Actor import and checkpoint lineage |
|
|
| ```text |
| Hannes running ONNX + exact frozen normalizer |
| → 2B race adaptation: fresh critic/optimizer, low LR/std |
| → checkpoint750:1.096s5/5 |
| → B3: full PPO resume, lower LR, small fixed std |
| → checkpoint800:1.092s5/5 |
| → D1: full PPO resume, early reward2.5→5 |
| → checkpoint900:1.084s5/5 |
| ``` |
|
|
| The import reconstructs the[512,256,128,14] actor MLP and preserves ONNX normalization. |
| Torch/ONNX parity is checked before training. External ONNX weights get a fresh critic |
| and optimizer; our own checkpoint continuations keep actor, critic and Adam moments. |
| The intended LR is set after loading, since optimizer load otherwise restores the old LR. |
|
|
| D1used1024envs,LR3.3333333333e-6,fixedstd0.05,forwardweight5,early-forwardweight5, |
| action-rateweight−0.01 and export/score every 50 iterations. Early reward covers the free |
| settle and first0.3 timed seconds. The exact historical command and configs are retained |
| in `records/sprint-2m/selected-run/`. |
|
|
| ```bash |
| # Historical D1 shape: parent is B3model800, not D1model900. |
| python scripts/finetune_sprint.py \ |
| --run-name d1_launchboost \ |
| --parent-checkpoint "$MICRODUCK_ROOT/best/phase3_b3_polish/checkpoint.pt" \ |
| --full-resume --num-envs 1024 --iterations 1001 \ |
| --lr 3.3333333333333333e-6 --std 0.05 --fixed-std \ |
| --early-forward-weight 5 --save-interval 50 \ |
| --target-score 0.90 --stage autonomous |
| ``` |
|
|
| Run it from the workspace layout described in [LOCAL_WORKSPACE](LOCAL_WORKSPACE.md), with |
| the correct checkout on `PYTHONPATH`. That target-score value was an automatic training |
| stop, not the actual result. Reproducing a training command does not guarantee identical |
| learned parameters; downloading and scoring the selected ONNX reproduces the policy. |
|
|
| ## Diagnosis: where time was lost |
|
|
| The detailed diagnosis of the nearby B3best800 policy measured, across seeds: |
|
|
| - settle-end forward speed1.34–1.38 m/s; |
| -100 ms peak forward speed2.04–2.10 m/s; |
| - time to95%of peak0.60–0.80 s after settle; |
| - roughly 6.7distinct touchdown events/s and0.28 m trunk advance/event; |
| - no timed wall contact. |
|
|
| The short rollout's same-foot cycle frequency was3.25–3.34 Hz with0.56–0.58 m stride. |
| These are measured definitions, not interchangeable step/cycle labels. Slow launch and |
| late peak speed motivated D1's early-forward reward, which gained0.008 s over B3. |
| The target2/0.988≈2.024 m/s course average still requires a faster or earlier fast gait. |
|
|
| ## What did not work |
|
|
| Nearby checkpoints varied sharply:2B500 had only 2/5 finishes, while750 had5/5. Continued |
| safe/speed branches did not reliably retain that peak. A target-speed curriculum and |
| scratch discovery attempts did not yield a selected faster policy in this snapshot. |
|
|
| Later root experiments included stronger early reward, increased exploration, an Arena |
| fall-clock repair, a signed quadratic velocity reward and a larger PPO LR. They were |
| still being evaluated when this documentation snapshot was assembled; the selected |
| policy remained D1 model 900. |
|
|
| We also tried CPU-only ONNX searches around D1: |
|
|
| | Single knob | Outcome | |
| |---|---| |
| |Leg action-amplitude gain|gain1.0 remained best; larger gains often fell| |
| |B3→D1 actor interpolation/extrapolation|D1alpha1.0 remained best| |
| |Raw joint-velocity feedback scaling|gain1.0 remained best; most alternatives lost reliability| |
| |Final actor leg-bias coordinate search|61 scores, no accepted improvement over1.084 s| |
|
|
| This evidence argues against more undirected static trims around the same gait. Change |
| the training incentive or search a different gait, and continue choosing by exact Arena |
| score. Only 5/5 results count as reliable bests. |
|
|
| ## Score and record |
|
|
| ```bash |
| uv run --group workshop arena score path/to/policy.onnx \ |
| --event sprint-2m --store verification-store |
| ``` |
|
|
| For repeatable media, use `tools/render_arena.py` with `--event sprint-2 m --seed 5 |
| --command .2 --mean-score 1.084`. It captures actual Arena state and replays it in a |
| visual-only mesh model. See [WEBSITE](WEBSITE.md) for CPU rendering and video integration. |
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