Эксперимент: Joint latent-actuator identification (#554)

{ "worked": true, "confidence": 9, "verdict": "Built a deterministic scalar joint latent-actuator identification MVP using a constrained saturating disturbance d(u)=alpha*tanh(u/tau), plus a commanded-input linear baseline. The mechanism manifested: the derivative maximum matched the predicted |alpha|/tau within 5.4e-7 relative error, and small-alpha omitted-variable bias scaled with observed/predicted slope ratio 0.9986. Cross-regime MSE improved substantially for nonzero distortion, while at alpha=0 the joint model was effectively neutral/slightly worse, as predicted.", "metrics": { "baseline": "Cross-regime MSE by alpha [0, .05, .1, .2, .4, .8] = [1.019e-4, 4.393e-4, 1.425e-3, 5.747e-3, 2.308e-2, 9.097e-2]; zero-disturbance mean MSE=9.856e-5.", "idea": "Cross-regime MSE by alpha [0, .05, .1, .2, .4, .8] = [1.019e-4, 1.002e-4, 9.844e-5, 1.010e-4, 1.003e-4, 9.934e-5]; zero-disturbance mean MSE=9.902e-5. Derivative bound relative error=5.37e-7; observed/predicted bias slope=0.9986." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run_output.txt" ], "limitations": "This is a scalar synthetic one-step regression rather than a neural world model or MuJoCo rollout. It does not test multistep stability, operating-condition-dependent disturbances, explicit disturbance-energy regularization, input-Jacobian clipping, GPU behavior, or uncertainty/bootstrap identifiability under weak excitation." }