Эксперимент: RNCOA Aggregated Collision Loss (#1374)

{ "worked": false, "confidence": 10, "verdict": "Built rncoa_experiment.py implementing the stated RNCOA soft-extrema loss, independent per-vertex hinge baseline, exact 1D body collision test, smoothing verification, random confusion test, and direct optimization comparison. The math check showed soft max/min errors falling from about 1.34e-3 at tau=0.2 to numerical zero at tau<=0.05, but 100% of sampled colliding bodies had zero RNCOA loss while safe bodies were often penalized. In optimization, RNCOA remained at the colliding initialization whereas the baseline reduced its hinge loss, although its independent objective still permits a straddling body; therefore the idea did not work with the authoritative signs.", "metrics": { "baseline": "Initial independent hinge 1.60 -> final 0.0; optimized vertices [-0.25335, 1.27876, 1.27606, -0.24983], exact collision=true because independent vertex penalties do not enforce whole-body separation.", "idea": "Initial/final stated RNCOA loss 0.0; vertices stayed [0.45, 0.55, 0.65, 0.35], exact collision=true. Random test: zero-loss rate on collisions 1.0 (5219/5219), while 1.05% of safe samples had zero loss." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 rncoa_experiment.py", "files": [ "rncoa_experiment.py", "results.json" ], "limitations": "No full neural trajectory MLP, 2D rotated rigid-body experiment, throughput, gradient-variance, or GPU benchmark was run because the authoritative loss failed the prerequisite geometric sanity check. The test uses a one-dimensional obstacle coordinate and zero learned slacks; correcting the inequality directions would require a separate experiment and is not treated as validation of the supplied formula." }