3d-lateral-balance

name

3d-lateral-balance

description

Resume the machin-walker 3D lateral-balance investigation (making the humanoid balance sideways while walking, not just in the sagittal plane). Read BEFORE touching any src/*3.src, ml/sag3*, ml/testb, ml/lat_spike, ml/cadence_sweep, or training the 3D rig on rbm21. Captures the honest state (PAUSED — mapped, not solved), everything already PROVEN, every dead-end already tried (don't repeat them), two hard-won infra gotchas (arena + chaos-sensitivity), the precisely located wall, the ranked next levers, and the rbm21 operating protocol.

machin-walker — 3D lateral balance

Status: PAUSED — the wall is mapped, not cracked (2026-07-10)

• DELIVERED & LIVE: 2D sagittal walk (0.62 Hz, <8% flight) + run, both certified, shipped, v1.0.0, live wasm (javimosch.github.io/machin-walker).
• OPEN FRONTIER: 3D lateral (frontal-plane) balance — walk in true 3D.
• All 3D work is on branch 3d-lateral-balance-wip (not merged to main).
• rbm21 is idle/clean; leftover training binaries were removed.

The goal & the reframe

Make the biped hold frontal-plane (lateral) balance while walking, so it walks in 3D rather than only the sagittal plane. The framing that reorganized the problem: DECOUPLE the planes. Sagittal balance (forward gait + step timing) and frontal balance (lateral foot placement / step width) are largely separable control channels in real bipeds. All four earlier attempts (before this arc) failed because they learned the coupled sagittal+frontal problem at once via black-box search.

What is PROVEN (rely on these)

1. Lateral authority EXISTS — ml/lat_spike.src (analytic frontal push-recovery): a CoM PD → antisymmetric hip ab/adduction + ankle inversion, plus a torso-roll PD on the spine, centers the CoM 3–5× better than passive under a shove. The old GA "can't get lateral authority / stuck at 0.32 assist" wall was a black-box-search artifact, not a physics limit. The lateral controller was never the blocker.
2. 0.40 Hz is the 3D walk cadence — ml/cadence_sweep.src: the heavier 3D rig (widened pelvis + tripod feet) wants a slower step than 2D's 0.62 Hz. Sweep: 0.35→17%, 0.40→~8% flight (the walk seed), 0.45→39%, 0.50→14%, 0.55–0.70 all bounce. Cadence is the analog of the lever that unlocked the 2D strict walk.

What FAILED — do NOT repeat

1. 2D→3D transfer (ml/testb.src): driving the 3D sagittal joints with the frozen certified 2D walker (walker_slow.json, exact 13-input/4-output bridge) walks BACKWARD (−2 to −6 m) and sags — in all four sagittal sign conventions. The 3D masses differ too much for the closed-loop 2D policy. Transfer is dead.
2. Native plain GA from random (ml/sag3_train.src, v1): descends + goes forward, but plateaus at a bounding lunge (fitness ~3.18).
3. CPG-clone + warm-start — the 2D recipe (ml/sag3_train2.src, v2), even at 0.40 Hz with the real ~8%-flight seed: STILL bounces — champion 7.6 m, 68% flight, falls; fitness plateaus at 3.16 (the same bounce ceiling as the 0.62 Hz run). Cadence alone does NOT crack it.
4. Hardcoded printed seed (ml/sag3_train3.src): ABANDONED — see gotcha #2.

Two hard-won infrastructure gotchas

1. Arena / main-goroutine loops. Building a biped3 per iteration in a main-goroutine loop WITHOUT arena{} accumulates (allocations aren't reclaimed until fn-return) → the CPG search built 2300 rigs → swap-thrashed rbm21 (host load 55, sshd wedged). FIX: arena{}-wrap every per-episode eval. And to return the best params, track them in scalars and ELEMENT-WRITE into a caller-pre-allocated slice — s3_cpg_search(samples, refines, maxTicks, out). Reassigning a best = []float{...} slice near arena{} returns stale/corrupt values (printed params didn't reproduce). Peak RSS after the fix: 72 MB.
2. Chaos-sensitivity (the big one). The 3D contact dynamics are chaotically sensitive to the last bits. A seed's printed 12-digit params evaluate to a TOTALLY different gait (in-memory best = 8% flight / survives 900 ticks; the same params printed→hardcoded = 30% flight / falls at tick 114). This is the documented "overfit to weight bits" phenomenon. CONSEQUENCE: a seed must stay IN-MEMORY through search → clone → evolve. NEVER round-trip params through text. That is why sag3_train2.src (in-memory) is correct and sag3_train3.src (hardcoded printed seed) is wrong.

The wall, precisely located

NOT lateral authority. NOT lateral-controller design. NOT cadence. The wall is: getting a stable native 3D sagittal WALK, blocked by

• (a) clone-divergence — the SGD clone is trained with random sensor values, so it IGNORES feedback; its closed-loop rollout drifts backward and falls even from the 8% seed (MSE is perfect ~4e-5, but open-loop-map ≠ closed-loop-stable).
• (b) the bounce basin — with the clone diverged, evolve maximizes distance-under-the-flight-cliff by leaping (ceiling ≈ 0.25 × 12.5 m ≈ 3.14), and the walk basin is unreachable from there by local mutation.

Next levers (ranked — pick ONE, deliberately)

1. Sensor-aware cloning (highest leverage). Clone from ACTUAL rollout trajectories (real sensor values along a stabilized/railed CPG run), not random sensors — so the net learns to USE feedback and doesn't diverge closed-loop. Attacks root cause (a). Swap only the dataset-generation step in sag3_train2.
2. Skip the clone. Direct gated-convergence evolve at 0.40 Hz with HARD hop-suppression (REJECT any >8%-flight episode, don't just penalize) + fresh seeds per round + gate on a reloaded champion (mirror 2D walk_train4).
3. Explicit ground-contact-time reward in s3_ep_score (reward foot-down fraction, not just penalize flight).
4. Then Stage B (only after a real sagittal walk stands): anneal the rail off while the analytic lateral capture-point controller (proven authority) + a small learned residual takes over. src/body3.src already has the frontal motors, tripod feet, and the twist stabilizer (bearing-alignment) staged.

Files (branch 3d-lateral-balance-wip)

• src/pbd3.src, src/body3.src — the 3D rig: PBD physics, widened pelvis, tripod feet, frontal hip/ankle motors, bearing-alignment twist stabilizer. Sensors: biped3_pitch/roll/yaw, w3_com, biped3_fallen.
• src/sag3.src — shared 3D-sagittal machinery: 13-input sensor vector (matches 2D body.src units), s3_apply, s3_rail (frontal-plane rail), CPG expert (cpg_hip/knee/ank), arena-safe s3_cpg_search(...,out), episodes, s3_fitness. Cadence is a global: g_s3_hz (default 0.62; set 0.40 for 3D).
• ml/lat_spike.src — lateral push-recovery spike (proves authority).
• ml/testb.src — the 2D→3D bridge (proves transfer fails).
• ml/sag3_train.src (v1 plain GA), ml/sag3_train2.src (v2 clone+warmstart @0.40).
• ml/cadence_sweep.src — the cadence sweep.
• ml/sag3_train3.src (ABANDONED, hardcoded seed), ml/diag.src (chaos diagnostic).
• ml/models/sag3*.json + *.jsonl — the (bouncing) champions + training logs.
• ml/vendor/{tinybrain,evolve}.src — vendored tinybrain (net + evolve_run + SGD).

Build any trainer: machin encode src/pbd3.src src/body3.src ml/vendor/tinybrain.src ml/vendor/evolve.src src/sag3.src ml/<trainer>.src > /tmp/t.mfl && machin build /tmp/t.mfl -o <bin>

rbm21 operating protocol (the training box) — BE GENTLE

• 14-core LXC (CT 201 on host pve2); SHARED PROD box (services: am-cloud, mago, hermes, machin-vault). Do not starve them.
• Caps: ≤8–10 workers, nice -n 12, ONE heavy job at a time, arena-wrap every main-loop episode build. It only had ~5–6 GB free — pop 96 fits, pop 128 thrashed.
• Detach: setsid nohup nice -12 ./bin > log 2>&1 < /dev/null &. Non-blocking: read the first 1–2 rounds, report ETA, stop; a re-check is one ssh of the jsonl.
• If sshd wedges (banner timeout), monitor via ssh pve2 'pct exec 201 -- ...'.
• If it thrashes (host load ≫ 14, pct exec hangs): recover with ssh pve2 'pct stop 201' → wait for load to settle → pct start 201 (all services auto-restart). This is user-authorized.
• Clean up after: rm the compiled binary from ~/ai/machin-walker/ when done (leftover binaries read as "still using resources").

How to resume

Pick lever #1 (sensor-aware cloning). The rig, the pipeline, the arena-safety, and the 0.40 Hz cadence are all solid and memory-safe — swap only the clone's dataset to real rollout sensors, re-run sag3_train2-style on rbm21 under the caps above, and check whether the champion's flight drops below 8% while distance grows. If a real sagittal walk stands, move to Stage B (lateral capture-point + residual).