comma controls challenge: sim-calibrated feedforward + PID (72.3 vs 110.3 baseline)

2026-06-16 13:30:25 -07:00
commit cea2c9c82a
13 changed files with 616 additions and 0 deletions
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+"""Coordinate-descent tuner for controllers/karti.py against the TinyPhysics sim.
+Deterministic (sim is seeded per-segment), so the search is noise-free.
+Runs to COST_END_IDX only (identical cost to a full rollout) to save compute.
+"""
+import json
+import sys
+import numpy as np
+from pathlib import Path
+from multiprocessing import Pool
+
+from tinyphysics import TinyPhysicsModel, TinyPhysicsSimulator, COST_END_IDX
+from controllers.karti import Controller
+
+MODEL_PATH = './models/tinyphysics.onnx'
+ALL = sorted(Path('data').glob('*.csv'))
+TUNE = ALL[:80]            # fast search set
+VAL = ALL[80:480]          # held-out validation
+
+_model = None
+def _init():
+    global _model
+    _model = TinyPhysicsModel(MODEL_PATH, debug=False)
+
+def _eval_one(args):
+    seg, params = args
+    sim = TinyPhysicsSimulator(_model, str(seg), controller=Controller(params), debug=False)
+    while sim.step_idx < COST_END_IDX:
+        sim.step()
+    c = sim.compute_cost()
+    return (c['total_cost'], c['lataccel_cost'], c['jerk_cost'])
+
+def evaluate(params, segs, pool):
+    res = np.array(pool.map(_eval_one, [(s, params) for s in segs]))
+    return res[:, 0].mean(), res[:, 1].mean(), res[:, 2].mean()
+
+GRIDS = {
+    'ff_scale': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3],
+    'kp':       [0.0, 0.05, 0.1, 0.2, 0.3],
+    'ki':       [0.0, 0.02, 0.05, 0.1],
+    'kd':       [-0.08, -0.05, -0.02, 0.0],
+    'preview':  [0, 1, 2, 3, 4],
+    'int_clip': [0.5, 1.0, 2.0],
+}
+ORDER = ['ff_scale', 'kp', 'ki', 'kd', 'preview', 'int_clip']
+
+def main():
+    cur = dict(ff_scale=1.0, kp=0.10, ki=0.05, kd=-0.02, preview=2, int_clip=1.0)
+    cache = {}
+    def ev(params):
+        key = tuple(sorted(params.items()))
+        if key not in cache:
+            cache[key] = evaluate(params, TUNE, pool)
+        return cache[key]
+
+    with Pool(4, initializer=_init) as pool:
+        base = ev(cur)
+        print(f"start {cur} -> total={base[0]:.3f} (lat={base[1]:.3f} jerk={base[2]:.3f})", flush=True)
+        for it in range(2):
+            print(f"\n===== pass {it+1} =====", flush=True)
+            for name in ORDER:
+                best_v, best_cost = cur[name], ev(cur)[0]
+                for v in GRIDS[name]:
+                    if v == cur[name]:
+                        continue
+                    trial = dict(cur); trial[name] = v
+                    cost = ev(trial)[0]
+                    tag = ""
+                    if cost < best_cost:
+                        best_cost, best_v, tag = cost, v, "  <-- best"
+                    print(f"  {name}={v!s:<7} total={cost:.3f}{tag}", flush=True)
+                cur[name] = best_v
+                print(f"  => {name} := {best_v}  (total={best_cost:.3f})", flush=True)
+
+        tcost = ev(cur)
+        print(f"\nFINAL params: {cur}", flush=True)
+        print(f"TUNE(80):  total={tcost[0]:.3f} lat={tcost[1]:.3f} jerk={tcost[2]:.3f}", flush=True)
+        vcost = evaluate(cur, VAL, pool)
+        print(f"VAL(400):  total={vcost[0]:.3f} lat={vcost[1]:.3f} jerk={vcost[2]:.3f}", flush=True)
+        Path('scratch/best_params.json').write_text(json.dumps(cur, indent=2))
+        print("saved scratch/best_params.json", flush=True)
+
+if __name__ == "__main__":
+    main()