Karti Tripathi
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
"""Final fine sweep: pin navg (4-9) + perturb kp/kd/ff_scale around the optimum."""
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import numpy as np
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from pathlib import Path
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from multiprocessing import Pool
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from tinyphysics import TinyPhysicsModel, TinyPhysicsSimulator, COST_END_IDX
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from controllers.karti import Controller
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MODEL_PATH = './models/tinyphysics.onnx'
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SEGS = sorted(Path('data').glob('*.csv'))[:100]
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NOC0 = (0.0, 0.70797, -0.006298, -0.000029)
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_model = None
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def _init():
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global _model
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_model = TinyPhysicsModel(MODEL_PATH, debug=False)
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def _ev(args):
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seg, params = args
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sim = TinyPhysicsSimulator(_model, str(seg), controller=Controller(params), debug=False)
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while sim.step_idx < COST_END_IDX:
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sim.step()
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c = sim.compute_cost()
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return c['total_cost'], c['lataccel_cost'], c['jerk_cost']
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def evalp(params, pool):
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r = np.array(pool.map(_ev, [(s, params) for s in SEGS]))
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return r[:, 0].mean(), r[:, 1].mean(), r[:, 2].mean()
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def b(**kw):
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d = dict(ff=NOC0); d.update(kw); return d
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TESTS = {
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'navg4': b(ff_navg=4),
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'navg5': b(ff_navg=5),
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'navg6': b(ff_navg=6),
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'navg7': b(ff_navg=7),
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'navg8': b(ff_navg=8),
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'navg9': b(ff_navg=9),
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'navg6 kp0.25': b(ff_navg=6, kp=0.25),
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'navg6 kp0.30': b(ff_navg=6, kp=0.30),
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'navg6 ffs1.05': b(ff_navg=6, ff_scale=1.05),
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'navg6 ffs1.10': b(ff_navg=6, ff_scale=1.10),
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'navg6 kd-0.03': b(ff_navg=6, kd=-0.03),
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'navg6 kp0.25 ffs1.05': b(ff_navg=6, kp=0.25, ff_scale=1.05),
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'navg5 kp0.25 ffs1.05': b(ff_navg=5, kp=0.25, ff_scale=1.05),
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'navg6 kp0.25 kd-0.04 ffs1.05': b(ff_navg=6, kp=0.25, kd=-0.04, ff_scale=1.05),
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}
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if __name__ == "__main__":
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with Pool(4, initializer=_init) as pool:
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print(f"{'config':<32} {'total':>8} {'lat':>7} {'jerk':>7}")
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print("-" * 58)
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best = None
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for name, p in TESTS.items():
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t, l, j = evalp(p, pool)
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star = ""
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if best is None or t < best[2]:
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best, star = (name, p, t), " <"
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print(f"{name:<32} {t:8.3f} {l:7.3f} {j:7.3f}{star}", flush=True)
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import json
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print(f"\nbest: {best[0]} ({best[2]:.3f})")
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print("PARAMS=" + json.dumps(best[1]))
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