fix+exp: 轮次30——统一认证与执行限位，到达57.4%不变；加密回放仍检出段内碰撞
This commit is contained in:
@@ -0,0 +1,62 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round30_after_rows.json",
|
||||
"out": "docs/experiment_round30_after.json",
|
||||
"trace": "ml/runs/round30_after.pt",
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.57421875,
|
||||
"collision_free_success": 0.57421875,
|
||||
"time_ok_1p3": 0.390625,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.4449527263641357,
|
||||
"time_mean_s": 2.3076188564300537,
|
||||
"brakes_mean": 19.25,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.20997506380081177,
|
||||
"err_fail_med": 229.68746948242188,
|
||||
"err_success_med": 0.18899640440940857,
|
||||
"dls_share": 0.625,
|
||||
"time_budget": "teacher×2(≥3 s)"
|
||||
},
|
||||
"simulation_wall_s": 248.83432349999202
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round30_before_rows.json",
|
||||
"out": "docs/experiment_round30_before.json",
|
||||
"trace": "ml/runs/round30_before.pt",
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.57421875,
|
||||
"collision_free_success": 0.56640625,
|
||||
"time_ok_1p3": 0.390625,
|
||||
"collision_rate": 0.02734375,
|
||||
"collision_steps_mean": 0.0625,
|
||||
"travel_ratio_median": 2.4449527263641357,
|
||||
"time_mean_s": 2.2993197441101074,
|
||||
"brakes_mean": 19.23828125,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.20997506380081177,
|
||||
"err_fail_med": 229.68746948242188,
|
||||
"err_success_med": 0.1950554996728897,
|
||||
"dls_share": 0.625,
|
||||
"time_budget": "teacher×2(≥3 s)"
|
||||
},
|
||||
"simulation_wall_s": 247.51454629999353
|
||||
}
|
||||
@@ -0,0 +1,880 @@
|
||||
{
|
||||
"trace": "ml/runs/round30_before.pt",
|
||||
"n": 256,
|
||||
"reported_collision_tasks": 7,
|
||||
"replayed_first_hits32": 7,
|
||||
"replayed_first_hits64": 7,
|
||||
"scope": "Replay reported first hits only; same geometry kernel at float32/float64, not a full safety audit.",
|
||||
"rows": [
|
||||
{
|
||||
"i": 21,
|
||||
"task": {
|
||||
"tid": 1223280991,
|
||||
"id": "s0001_000564",
|
||||
"cat": "A",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
178.767,
|
||||
101.917,
|
||||
25.283,
|
||||
31.588,
|
||||
-12.179,
|
||||
-6.464
|
||||
],
|
||||
"T": [
|
||||
320.779,
|
||||
79.294,
|
||||
350.853
|
||||
],
|
||||
"R": [
|
||||
-0.5548626629953249,
|
||||
-0.1382477148217311,
|
||||
-0.820374910976869,
|
||||
-0.29956278115972873,
|
||||
-0.8867511636570162,
|
||||
0.35204334093514156,
|
||||
-0.7761375943462505,
|
||||
0.44108949556693955,
|
||||
0.4506112421399604
|
||||
],
|
||||
"exec_s": 2.86,
|
||||
"travel": 581.023,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 5.659999847412109,
|
||||
"dls_it": -1,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.35770511627197266,
|
||||
-0.3957242965698242
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.3577231796451059,
|
||||
-0.39572636491366353
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
0,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
0,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
139.78468322753906,
|
||||
87.256591796875,
|
||||
6.928672790527344,
|
||||
103.78778839111328,
|
||||
-94.0,
|
||||
19.4688720703125
|
||||
],
|
||||
"endpoint": [
|
||||
139.50450134277344,
|
||||
87.4175796508789,
|
||||
6.71871280670166,
|
||||
104.29376983642578,
|
||||
-94.0,
|
||||
21.218488693237305
|
||||
],
|
||||
"dense_nsegments": 175,
|
||||
"dense_gap64_min_mm": -0.39572636491366353,
|
||||
"certificate": {
|
||||
"plan_start_it": 282,
|
||||
"step": 0,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
139.50450134277344,
|
||||
87.4175796508789,
|
||||
6.71871280670166,
|
||||
104.29376983642578,
|
||||
-95.717529296875,
|
||||
21.218488693237305
|
||||
],
|
||||
"midpoint": [
|
||||
139.64459228515625,
|
||||
87.33708190917969,
|
||||
6.823692798614502,
|
||||
104.04077911376953,
|
||||
-94.8587646484375,
|
||||
20.34368133544922
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.1606760025024414,
|
||||
0.5
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.16066912536394184,
|
||||
0.5
|
||||
],
|
||||
"command_delta": [
|
||||
-0.2801789343357086,
|
||||
0.16098570823669434,
|
||||
-0.20995977520942688,
|
||||
0.5059784054756165,
|
||||
-1.717526912689209,
|
||||
1.7496168613433838
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 1.717529296875
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 84,
|
||||
"task": {
|
||||
"tid": 1268963905,
|
||||
"id": "s0002_000538",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.198,
|
||||
52.395,
|
||||
108.796,
|
||||
75.534,
|
||||
82.302,
|
||||
-121.823
|
||||
],
|
||||
"T": [
|
||||
-85.062,
|
||||
275.378,
|
||||
888.929
|
||||
],
|
||||
"R": [
|
||||
0.5139412462799992,
|
||||
-0.4285630099475821,
|
||||
-0.7431003578769356,
|
||||
-0.5533754886400825,
|
||||
-0.8275489878032067,
|
||||
0.09454227286372051,
|
||||
-0.6554692700230204,
|
||||
0.36262435010704513,
|
||||
-0.6624678231921397
|
||||
],
|
||||
"exec_s": 3.2600000000000002,
|
||||
"travel": 349.09,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 5.980000019073486,
|
||||
"dls_it": -1,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.030836105346679688,
|
||||
-0.1565418243408203,
|
||||
-0.11790847778320312
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.030838235678215753,
|
||||
-0.15655898804830848,
|
||||
-0.11791701257236653
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
6,
|
||||
6,
|
||||
6
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
6,
|
||||
6,
|
||||
6
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
-69.87329864501953,
|
||||
36.559112548828125,
|
||||
2.094588279724121,
|
||||
102.22724151611328,
|
||||
89.0,
|
||||
-212.1793212890625
|
||||
],
|
||||
"endpoint": [
|
||||
-69.79582214355469,
|
||||
37.351837158203125,
|
||||
-0.033895254135131836,
|
||||
102.45509338378906,
|
||||
89.0,
|
||||
-214.75440979003906
|
||||
],
|
||||
"dense_nsegments": 258,
|
||||
"dense_gap64_min_mm": -0.16882067924099076,
|
||||
"certificate": {
|
||||
"plan_start_it": 298,
|
||||
"step": 0,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
-69.79582214355469,
|
||||
37.351837158203125,
|
||||
-0.033895254135131836,
|
||||
102.45509338378906,
|
||||
89.79981231689453,
|
||||
-214.75440979003906
|
||||
],
|
||||
"midpoint": [
|
||||
-69.83456420898438,
|
||||
36.955474853515625,
|
||||
1.0303465127944946,
|
||||
102.34117126464844,
|
||||
89.39990234375,
|
||||
-213.4668731689453
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.5,
|
||||
0.20721912384033203
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.5,
|
||||
0.2071966639119509
|
||||
],
|
||||
"command_delta": [
|
||||
0.07747504115104675,
|
||||
0.7927232980728149,
|
||||
-2.128483533859253,
|
||||
0.22785267233848572,
|
||||
0.7998088598251343,
|
||||
-2.5750951766967773
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 0.7998123168945312
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 145,
|
||||
"task": {
|
||||
"tid": 593213945,
|
||||
"id": "s0003_000414",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.395,
|
||||
84.783,
|
||||
2.744,
|
||||
14.841,
|
||||
-86.783,
|
||||
81.932
|
||||
],
|
||||
"T": [
|
||||
5.391,
|
||||
506.723,
|
||||
383.367
|
||||
],
|
||||
"R": [
|
||||
-0.5269859333114162,
|
||||
-0.7554822816510317,
|
||||
-0.38927156099983334,
|
||||
0.6402732880654818,
|
||||
-0.05172298162073713,
|
||||
-0.7664038424760652,
|
||||
0.5588702377848656,
|
||||
-0.6531242265324508,
|
||||
0.5109724082909718
|
||||
],
|
||||
"exec_s": 2.96,
|
||||
"travel": 560.55,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 5.599999904632568,
|
||||
"dls_it": -1,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.3180522918701172,
|
||||
-0.19827747344970703
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.318071359587325,
|
||||
-0.19825289671655533
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
0,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
0,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
-36.05080032348633,
|
||||
49.852294921875,
|
||||
22.054914474487305,
|
||||
67.3380126953125,
|
||||
-94.0,
|
||||
79.87274932861328
|
||||
],
|
||||
"endpoint": [
|
||||
-35.60316848754883,
|
||||
48.9223747253418,
|
||||
21.386226654052734,
|
||||
67.87915802001953,
|
||||
-94.0,
|
||||
79.27398681640625
|
||||
],
|
||||
"dense_nsegments": 93,
|
||||
"dense_gap64_min_mm": -0.19825289671655533,
|
||||
"certificate": {
|
||||
"plan_start_it": 278,
|
||||
"step": 1,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
-35.60316848754883,
|
||||
48.9223747253418,
|
||||
21.3862247467041,
|
||||
67.87915802001953,
|
||||
-97.17477416992188,
|
||||
79.27399444580078
|
||||
],
|
||||
"midpoint": [
|
||||
-35.82698440551758,
|
||||
49.387332916259766,
|
||||
21.720569610595703,
|
||||
67.60858154296875,
|
||||
-96.70620727539062,
|
||||
79.57337188720703
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.2614564895629883,
|
||||
0.5
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.2614702651611598,
|
||||
0.5
|
||||
],
|
||||
"command_delta": [
|
||||
0.4476320445537567,
|
||||
-0.929920494556427,
|
||||
-0.6686887741088867,
|
||||
0.5411425828933716,
|
||||
-0.9371238946914673,
|
||||
-0.5987589359283447
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 3.174774169921875
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 164,
|
||||
"task": {
|
||||
"tid": 1743026973,
|
||||
"id": "s0003_000814",
|
||||
"cat": "C",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-65.982,
|
||||
5.595,
|
||||
-64.669,
|
||||
-72.912,
|
||||
85.732,
|
||||
-86.377
|
||||
],
|
||||
"T": [
|
||||
-189.354,
|
||||
221.034,
|
||||
302.457
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.3000000000000003,
|
||||
"travel": 306.824,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 3.0399999618530273,
|
||||
"dls_it": -1,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5,
|
||||
-0.12672901153564453
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5,
|
||||
-0.12673094091899273
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
5
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
5
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
-33.6672248840332,
|
||||
39.63850021362305,
|
||||
-60.174774169921875,
|
||||
-108.2870864868164,
|
||||
88.27082061767578,
|
||||
-74.56200408935547
|
||||
],
|
||||
"endpoint": [
|
||||
-32.8936882019043,
|
||||
39.478729248046875,
|
||||
-58.639915466308594,
|
||||
-107.65913391113281,
|
||||
89.0,
|
||||
-74.92655944824219
|
||||
],
|
||||
"dense_nsegments": 154,
|
||||
"dense_gap64_min_mm": -0.12673094091899273,
|
||||
"certificate": {
|
||||
"plan_start_it": 150,
|
||||
"step": 1,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
-32.89369201660156,
|
||||
39.478729248046875,
|
||||
-58.639915466308594,
|
||||
-107.65913391113281,
|
||||
89.33978271484375,
|
||||
-74.92655944824219
|
||||
],
|
||||
"midpoint": [
|
||||
-33.28045654296875,
|
||||
39.55861282348633,
|
||||
-59.407344818115234,
|
||||
-107.97311401367188,
|
||||
88.8052978515625,
|
||||
-74.7442855834961
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.1356372833251953,
|
||||
0.5
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.13560383338968585,
|
||||
0.5
|
||||
],
|
||||
"command_delta": [
|
||||
0.773534893989563,
|
||||
-0.15977251529693604,
|
||||
1.534859299659729,
|
||||
0.6279491186141968,
|
||||
1.068959355354309,
|
||||
-0.3645569384098053
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 0.33978271484375
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 175,
|
||||
"task": {
|
||||
"tid": 444406422,
|
||||
"id": "s0003_001100",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-28.854,
|
||||
43.33,
|
||||
-137.874,
|
||||
-6.895,
|
||||
-88.001,
|
||||
-43.745
|
||||
],
|
||||
"T": [
|
||||
379.105,
|
||||
293.88,
|
||||
653.541
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.58,
|
||||
"travel": 465.057,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 0.8399999737739563,
|
||||
"dls_it": 144,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.4698514938354492,
|
||||
-0.6612262725830078,
|
||||
-0.9359521865844727
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.4698356780126254,
|
||||
-0.6612412101671552,
|
||||
-0.9359581587234
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
6,
|
||||
6,
|
||||
6
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
6,
|
||||
6,
|
||||
6
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
-42.48794937133789,
|
||||
11.756612777709961,
|
||||
-124.7530288696289,
|
||||
-46.12489700317383,
|
||||
-94.0,
|
||||
-54.57075500488281
|
||||
],
|
||||
"endpoint": [
|
||||
-42.73639678955078,
|
||||
10.6148681640625,
|
||||
-124.47537231445312,
|
||||
-48.52891159057617,
|
||||
-94.0,
|
||||
-55.451263427734375
|
||||
],
|
||||
"dense_nsegments": 241,
|
||||
"dense_gap64_min_mm": -0.9544276230224238,
|
||||
"certificate": {
|
||||
"plan_start_it": 40,
|
||||
"step": 1,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
-42.73639678955078,
|
||||
10.6148681640625,
|
||||
-124.47537231445312,
|
||||
-48.52891159057617,
|
||||
-98.31655883789062,
|
||||
-55.45126724243164
|
||||
],
|
||||
"midpoint": [
|
||||
-42.61217498779297,
|
||||
11.18574047088623,
|
||||
-124.61420440673828,
|
||||
-47.326904296875,
|
||||
-97.59821319580078,
|
||||
-55.011009216308594
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"command_delta": [
|
||||
-0.24844767153263092,
|
||||
-1.1417450904846191,
|
||||
0.27765607833862305,
|
||||
-2.4040162563323975,
|
||||
-1.4366930723190308,
|
||||
-0.8805096745491028
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 4.316558837890625
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 180,
|
||||
"task": {
|
||||
"tid": 737760595,
|
||||
"id": "s0003_001280",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
41.213,
|
||||
49.353,
|
||||
-149.808,
|
||||
-8.981,
|
||||
-88.566,
|
||||
83.45
|
||||
],
|
||||
"T": [
|
||||
118.145,
|
||||
2.733,
|
||||
266.082
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.78,
|
||||
"travel": 228.67,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 1.1399999856948853,
|
||||
"dls_it": 102,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.06517887115478516,
|
||||
-0.0172576904296875,
|
||||
0.013133049011230469
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.06521150921177998,
|
||||
-0.017227151210720493,
|
||||
0.013145878955246104
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
5,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
5,
|
||||
5,
|
||||
5
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
1.8212896585464478,
|
||||
-9.327469825744629,
|
||||
-150.87704467773438,
|
||||
-23.00735855102539,
|
||||
-94.0,
|
||||
93.73664855957031
|
||||
],
|
||||
"endpoint": [
|
||||
1.3598560094833374,
|
||||
-9.66404914855957,
|
||||
-150.52992248535156,
|
||||
-23.200042724609375,
|
||||
-94.0,
|
||||
92.69589233398438
|
||||
],
|
||||
"dense_nsegments": 105,
|
||||
"dense_gap64_min_mm": -0.020246891242656773,
|
||||
"certificate": {
|
||||
"plan_start_it": 56,
|
||||
"step": 0,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
1.3598560094833374,
|
||||
-9.66404914855957,
|
||||
-150.52992248535156,
|
||||
-23.200042724609375,
|
||||
-95.86970520019531,
|
||||
92.69589233398438
|
||||
],
|
||||
"midpoint": [
|
||||
1.5905728340148926,
|
||||
-9.495759010314941,
|
||||
-150.70347595214844,
|
||||
-23.103700637817383,
|
||||
-94.93485260009766,
|
||||
93.21627044677734
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.5,
|
||||
0.20821666717529297
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.5,
|
||||
0.20822553884000428
|
||||
],
|
||||
"command_delta": [
|
||||
-0.46143364906311035,
|
||||
-0.33657926321029663,
|
||||
0.3471294939517975,
|
||||
-0.19268345832824707,
|
||||
-1.8697090148925781,
|
||||
-1.0407572984695435
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 1.8697052001953125
|
||||
}
|
||||
},
|
||||
{
|
||||
"i": 215,
|
||||
"task": {
|
||||
"tid": 169730876,
|
||||
"id": "s0004_000544",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
47.051,
|
||||
-1.179,
|
||||
-94.31,
|
||||
-43.128,
|
||||
67.067,
|
||||
-119.867
|
||||
],
|
||||
"T": [
|
||||
-142.696,
|
||||
-139.159,
|
||||
726.163
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.16,
|
||||
"travel": 461.684,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"first_hit_s": 3.5399999618530273,
|
||||
"dls_it": -1,
|
||||
"point_order": [
|
||||
"q0",
|
||||
"previous",
|
||||
"midpoint",
|
||||
"endpoint"
|
||||
],
|
||||
"gap32_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5,
|
||||
-0.08817481994628906
|
||||
],
|
||||
"gap64_mm": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5,
|
||||
-0.08817155079043282
|
||||
],
|
||||
"pair32": [
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
6
|
||||
],
|
||||
"pair64": [
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
6
|
||||
],
|
||||
"boundary_joints": [
|
||||
5
|
||||
],
|
||||
"previous": [
|
||||
34.57734298706055,
|
||||
32.23564910888672,
|
||||
-71.76512908935547,
|
||||
10.783411979675293,
|
||||
88.44934844970703,
|
||||
-105.16633605957031
|
||||
],
|
||||
"endpoint": [
|
||||
34.31901931762695,
|
||||
33.24732208251953,
|
||||
-72.12022399902344,
|
||||
10.171412467956543,
|
||||
89.0,
|
||||
-106.89080810546875
|
||||
],
|
||||
"dense_nsegments": 173,
|
||||
"dense_gap64_min_mm": -0.08817155079043282,
|
||||
"certificate": {
|
||||
"plan_start_it": 176,
|
||||
"step": 0,
|
||||
"checked": true,
|
||||
"endpoint": [
|
||||
34.31901931762695,
|
||||
33.24732208251953,
|
||||
-72.12022399902344,
|
||||
10.171412467956543,
|
||||
89.79435729980469,
|
||||
-106.89080810546875
|
||||
],
|
||||
"midpoint": [
|
||||
34.44818115234375,
|
||||
32.741485595703125,
|
||||
-71.94267272949219,
|
||||
10.477412223815918,
|
||||
89.12185668945312,
|
||||
-106.02857208251953
|
||||
],
|
||||
"recorded_gap32_mm": [
|
||||
0.2809743881225586,
|
||||
0.5
|
||||
],
|
||||
"replayed_gap64_mm": [
|
||||
0.28098007564065064,
|
||||
0.5
|
||||
],
|
||||
"command_delta": [
|
||||
-0.2583238482475281,
|
||||
1.011674404144287,
|
||||
-0.3550943434238434,
|
||||
-0.6119998693466187,
|
||||
1.3450126647949219,
|
||||
-1.7244750261306763
|
||||
],
|
||||
"clamped_reconstruction_max_abs_deg": 0.0,
|
||||
"certified_vs_executed_max_abs_deg": 0.7943572998046875
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,65 @@
|
||||
{
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"same_config_except_output_paths": true,
|
||||
"instrumentation_vs_round29_first_batch_max_abs_deg": 0.0,
|
||||
"reached_only_after": [],
|
||||
"reached_only_before": [],
|
||||
"collision_tasks_before": [
|
||||
21,
|
||||
84,
|
||||
145,
|
||||
164,
|
||||
175,
|
||||
180,
|
||||
215
|
||||
],
|
||||
"collision_tasks_after": [],
|
||||
"statistics": {
|
||||
"before": {
|
||||
"active_certified_steps": 34069,
|
||||
"certificate_endpoint_mismatch_gt_0p001deg": 5298,
|
||||
"certificate_endpoint_max_abs_deg": 7.587158203125,
|
||||
"certificate_midpoint_max_abs_deg": 5.78558349609375,
|
||||
"uncertified_moving_steps": 0,
|
||||
"executed_speed_ratio_max": 1.660003662109375,
|
||||
"executed_speed_limit_violating_tasks": 256,
|
||||
"executed_speed_max_deg_s": [
|
||||
99.6002197265625,
|
||||
99.60002899169922,
|
||||
149.40032958984375,
|
||||
149.400146484375,
|
||||
199.20005798339844,
|
||||
298.80029296875
|
||||
],
|
||||
"executed_travel_ratio_median_success": 2.4250845909118652,
|
||||
"commanded_travel_ratio_median_success": 2.4449527263641357
|
||||
},
|
||||
"after": {
|
||||
"active_certified_steps": 34107,
|
||||
"certificate_endpoint_mismatch_gt_0p001deg": 0,
|
||||
"certificate_endpoint_max_abs_deg": 0.0,
|
||||
"certificate_midpoint_max_abs_deg": 0.0,
|
||||
"uncertified_moving_steps": 0,
|
||||
"executed_speed_ratio_max": 1.660003662109375,
|
||||
"executed_speed_limit_violating_tasks": 256,
|
||||
"executed_speed_max_deg_s": [
|
||||
99.6002197265625,
|
||||
99.60002899169922,
|
||||
149.40032958984375,
|
||||
149.400146484375,
|
||||
199.20005798339844,
|
||||
298.80029296875
|
||||
],
|
||||
"executed_travel_ratio_median_success": 2.4250845909118652,
|
||||
"commanded_travel_ratio_median_success": 2.4449527263641357
|
||||
}
|
||||
},
|
||||
"cpu_prediction_microbenchmark": {
|
||||
"scope": "B=1, commit=2, 100 warmups then 1000 serial calls, CPU one thread; not full planning latency",
|
||||
"before_p50_ms": 0.0388,
|
||||
"before_p99_ms": 0.069102,
|
||||
"after_p50_ms": 0.0518,
|
||||
"after_p99_ms": 0.093933
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,485 @@
|
||||
{
|
||||
"trace": "ml/runs/round30_after.pt",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"indices": [
|
||||
0,
|
||||
21,
|
||||
84,
|
||||
128,
|
||||
145,
|
||||
164,
|
||||
175,
|
||||
180,
|
||||
197,
|
||||
215,
|
||||
224,
|
||||
255
|
||||
],
|
||||
"max_step_deg": 0.05,
|
||||
"dtype": "float64",
|
||||
"device": "cuda",
|
||||
"scope": "Executed trajectory through each task deadline or arrival, including initial state; finite sampling, not continuous certification.",
|
||||
"collision_tasks": 1,
|
||||
"rows": [
|
||||
{
|
||||
"i": 0,
|
||||
"task": {
|
||||
"tid": 919549409,
|
||||
"id": "s0001_000001",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-129.897,
|
||||
46.68,
|
||||
43.494,
|
||||
-97.461,
|
||||
-85.102,
|
||||
-139.71
|
||||
],
|
||||
"T": [
|
||||
-116.172,
|
||||
268.408,
|
||||
179.282
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 3.48,
|
||||
"travel": 345.324,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 174,
|
||||
"sampled_points": 6211,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.02673023799951224,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 21,
|
||||
"task": {
|
||||
"tid": 1223280991,
|
||||
"id": "s0001_000564",
|
||||
"cat": "A",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
178.767,
|
||||
101.917,
|
||||
25.283,
|
||||
31.588,
|
||||
-12.179,
|
||||
-6.464
|
||||
],
|
||||
"T": [
|
||||
320.779,
|
||||
79.294,
|
||||
350.853
|
||||
],
|
||||
"R": [
|
||||
-0.5548626629953249,
|
||||
-0.1382477148217311,
|
||||
-0.820374910976869,
|
||||
-0.29956278115972873,
|
||||
-0.8867511636570162,
|
||||
0.35204334093514156,
|
||||
-0.7761375943462505,
|
||||
0.44108949556693955,
|
||||
0.4506112421399604
|
||||
],
|
||||
"exec_s": 2.86,
|
||||
"travel": 581.023,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 286,
|
||||
"sampled_points": 6059,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.009057896844284485,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 84,
|
||||
"task": {
|
||||
"tid": 1268963905,
|
||||
"id": "s0002_000538",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.198,
|
||||
52.395,
|
||||
108.796,
|
||||
75.534,
|
||||
82.302,
|
||||
-121.823
|
||||
],
|
||||
"T": [
|
||||
-85.062,
|
||||
275.378,
|
||||
888.929
|
||||
],
|
||||
"R": [
|
||||
0.5139412462799992,
|
||||
-0.4285630099475821,
|
||||
-0.7431003578769356,
|
||||
-0.5533754886400825,
|
||||
-0.8275489878032067,
|
||||
0.09454227286372051,
|
||||
-0.6554692700230204,
|
||||
0.36262435010704513,
|
||||
-0.6624678231921397
|
||||
],
|
||||
"exec_s": 3.2600000000000002,
|
||||
"travel": 349.09,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 326,
|
||||
"sampled_points": 6251,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.006206321604070197,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 128,
|
||||
"task": {
|
||||
"tid": 408952743,
|
||||
"id": "s0002_001588",
|
||||
"cat": "A",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-153.539,
|
||||
100.454,
|
||||
108.434,
|
||||
3.379,
|
||||
70.254,
|
||||
-179.848
|
||||
],
|
||||
"T": [
|
||||
-324.009,
|
||||
70.778,
|
||||
919.477
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 0.8200000000000001,
|
||||
"travel": 121.056,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 63,
|
||||
"sampled_points": 2403,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.5,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 145,
|
||||
"task": {
|
||||
"tid": 593213945,
|
||||
"id": "s0003_000414",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.395,
|
||||
84.783,
|
||||
2.744,
|
||||
14.841,
|
||||
-86.783,
|
||||
81.932
|
||||
],
|
||||
"T": [
|
||||
5.391,
|
||||
506.723,
|
||||
383.367
|
||||
],
|
||||
"R": [
|
||||
-0.5269859333114162,
|
||||
-0.7554822816510317,
|
||||
-0.38927156099983334,
|
||||
0.6402732880654818,
|
||||
-0.05172298162073713,
|
||||
-0.7664038424760652,
|
||||
0.5588702377848656,
|
||||
-0.6531242265324508,
|
||||
0.5109724082909718
|
||||
],
|
||||
"exec_s": 2.96,
|
||||
"travel": 560.55,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 296,
|
||||
"sampled_points": 4509,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.002145835308116517,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 164,
|
||||
"task": {
|
||||
"tid": 1743026973,
|
||||
"id": "s0003_000814",
|
||||
"cat": "C",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-65.982,
|
||||
5.595,
|
||||
-64.669,
|
||||
-72.912,
|
||||
85.732,
|
||||
-86.377
|
||||
],
|
||||
"T": [
|
||||
-189.354,
|
||||
221.034,
|
||||
302.457
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.3000000000000003,
|
||||
"travel": 306.824,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 230,
|
||||
"sampled_points": 3985,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.008857213584009926,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 175,
|
||||
"task": {
|
||||
"tid": 444406422,
|
||||
"id": "s0003_001100",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-28.854,
|
||||
43.33,
|
||||
-137.874,
|
||||
-6.895,
|
||||
-88.001,
|
||||
-43.745
|
||||
],
|
||||
"T": [
|
||||
379.105,
|
||||
293.88,
|
||||
653.541
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.58,
|
||||
"travel": 465.057,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 143,
|
||||
"sampled_points": 5547,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.18812628629569694,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 180,
|
||||
"task": {
|
||||
"tid": 737760595,
|
||||
"id": "s0003_001280",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
41.213,
|
||||
49.353,
|
||||
-149.808,
|
||||
-8.981,
|
||||
-88.566,
|
||||
83.45
|
||||
],
|
||||
"T": [
|
||||
118.145,
|
||||
2.733,
|
||||
266.082
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.78,
|
||||
"travel": 228.67,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 95,
|
||||
"sampled_points": 3153,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.1492959159660998,
|
||||
"collision_segments": 1,
|
||||
"first_collision_s": 1.0915789473684212
|
||||
},
|
||||
{
|
||||
"i": 197,
|
||||
"task": {
|
||||
"tid": 1433345185,
|
||||
"id": "s0004_000020",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
20.112,
|
||||
-20.013,
|
||||
-177.85,
|
||||
32.746,
|
||||
-70.888,
|
||||
-30.111
|
||||
],
|
||||
"T": [
|
||||
-288.364,
|
||||
63.606,
|
||||
672.54
|
||||
],
|
||||
"R": [
|
||||
-0.5335375732231524,
|
||||
-0.7509294450860695,
|
||||
0.38915630081225366,
|
||||
-0.8443344656004893,
|
||||
0.4997538915602813,
|
||||
-0.19324947107169785,
|
||||
-0.049365657681080755,
|
||||
-0.4316839311036288,
|
||||
-0.9006730902323173
|
||||
],
|
||||
"exec_s": 2.14,
|
||||
"travel": 634.022,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 214,
|
||||
"sampled_points": 4641,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.0010684324191032601,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 215,
|
||||
"task": {
|
||||
"tid": 169730876,
|
||||
"id": "s0004_000544",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
47.051,
|
||||
-1.179,
|
||||
-94.31,
|
||||
-43.128,
|
||||
67.067,
|
||||
-119.867
|
||||
],
|
||||
"T": [
|
||||
-142.696,
|
||||
-139.159,
|
||||
726.163
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.16,
|
||||
"travel": 461.684,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 216,
|
||||
"sampled_points": 6605,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.2112382958686112,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 224,
|
||||
"task": {
|
||||
"tid": 1884410379,
|
||||
"id": "s0004_000721",
|
||||
"cat": "A",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-127.447,
|
||||
7.845,
|
||||
-124.228,
|
||||
-20.968,
|
||||
-21.51,
|
||||
-35.505
|
||||
],
|
||||
"T": [
|
||||
-233.158,
|
||||
-59.78,
|
||||
648.987
|
||||
],
|
||||
"R": [
|
||||
0.3964618999442966,
|
||||
-0.020495209950317686,
|
||||
0.9178223729358808,
|
||||
-0.8179090164279511,
|
||||
0.4461640898622928,
|
||||
0.3632663564978371,
|
||||
-0.41694460392049726,
|
||||
-0.8947164641865291,
|
||||
0.1601238457417738
|
||||
],
|
||||
"exec_s": 1.48,
|
||||
"travel": 356.988,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 128,
|
||||
"sampled_points": 4707,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.0041216155493710716,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 255,
|
||||
"task": {
|
||||
"tid": 712677274,
|
||||
"id": "s0004_001686",
|
||||
"cat": "C",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-65.548,
|
||||
71.865,
|
||||
27.462,
|
||||
-75.048,
|
||||
60.325,
|
||||
151.872
|
||||
],
|
||||
"T": [
|
||||
-53.154,
|
||||
50.204,
|
||||
397.489
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.84,
|
||||
"travel": 238.182,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 184,
|
||||
"sampled_points": 2495,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.004616787795093558,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
}
|
||||
],
|
||||
"audit_wall_s": 15.056756100006169
|
||||
}
|
||||
@@ -0,0 +1,485 @@
|
||||
{
|
||||
"trace": "ml/runs/round30_before.pt",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"indices": [
|
||||
0,
|
||||
21,
|
||||
84,
|
||||
128,
|
||||
145,
|
||||
164,
|
||||
175,
|
||||
180,
|
||||
197,
|
||||
215,
|
||||
224,
|
||||
255
|
||||
],
|
||||
"max_step_deg": 0.05,
|
||||
"dtype": "float64",
|
||||
"device": "cuda",
|
||||
"scope": "Executed trajectory through each task deadline or arrival, including initial state; finite sampling, not continuous certification.",
|
||||
"collision_tasks": 7,
|
||||
"rows": [
|
||||
{
|
||||
"i": 0,
|
||||
"task": {
|
||||
"tid": 919549409,
|
||||
"id": "s0001_000001",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-129.897,
|
||||
46.68,
|
||||
43.494,
|
||||
-97.461,
|
||||
-85.102,
|
||||
-139.71
|
||||
],
|
||||
"T": [
|
||||
-116.172,
|
||||
268.408,
|
||||
179.282
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 3.48,
|
||||
"travel": 345.324,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 174,
|
||||
"sampled_points": 6211,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.02673023799951224,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 21,
|
||||
"task": {
|
||||
"tid": 1223280991,
|
||||
"id": "s0001_000564",
|
||||
"cat": "A",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
178.767,
|
||||
101.917,
|
||||
25.283,
|
||||
31.588,
|
||||
-12.179,
|
||||
-6.464
|
||||
],
|
||||
"T": [
|
||||
320.779,
|
||||
79.294,
|
||||
350.853
|
||||
],
|
||||
"R": [
|
||||
-0.5548626629953249,
|
||||
-0.1382477148217311,
|
||||
-0.820374910976869,
|
||||
-0.29956278115972873,
|
||||
-0.8867511636570162,
|
||||
0.35204334093514156,
|
||||
-0.7761375943462505,
|
||||
0.44108949556693955,
|
||||
0.4506112421399604
|
||||
],
|
||||
"exec_s": 2.86,
|
||||
"travel": 581.023,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 286,
|
||||
"sampled_points": 6099,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.39572636491366353,
|
||||
"collision_segments": 2,
|
||||
"first_collision_s": 5.655
|
||||
},
|
||||
{
|
||||
"i": 84,
|
||||
"task": {
|
||||
"tid": 1268963905,
|
||||
"id": "s0002_000538",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.198,
|
||||
52.395,
|
||||
108.796,
|
||||
75.534,
|
||||
82.302,
|
||||
-121.823
|
||||
],
|
||||
"T": [
|
||||
-85.062,
|
||||
275.378,
|
||||
888.929
|
||||
],
|
||||
"R": [
|
||||
0.5139412462799992,
|
||||
-0.4285630099475821,
|
||||
-0.7431003578769356,
|
||||
-0.5533754886400825,
|
||||
-0.8275489878032067,
|
||||
0.09454227286372051,
|
||||
-0.6554692700230204,
|
||||
0.36262435010704513,
|
||||
-0.6624678231921397
|
||||
],
|
||||
"exec_s": 3.2600000000000002,
|
||||
"travel": 349.09,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 326,
|
||||
"sampled_points": 6037,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.16878710314368206,
|
||||
"collision_segments": 3,
|
||||
"first_collision_s": 5.961153846153847
|
||||
},
|
||||
{
|
||||
"i": 128,
|
||||
"task": {
|
||||
"tid": 408952743,
|
||||
"id": "s0002_001588",
|
||||
"cat": "A",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-153.539,
|
||||
100.454,
|
||||
108.434,
|
||||
3.379,
|
||||
70.254,
|
||||
-179.848
|
||||
],
|
||||
"T": [
|
||||
-324.009,
|
||||
70.778,
|
||||
919.477
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 0.8200000000000001,
|
||||
"travel": 121.056,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 63,
|
||||
"sampled_points": 2403,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.5,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 145,
|
||||
"task": {
|
||||
"tid": 593213945,
|
||||
"id": "s0003_000414",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-49.395,
|
||||
84.783,
|
||||
2.744,
|
||||
14.841,
|
||||
-86.783,
|
||||
81.932
|
||||
],
|
||||
"T": [
|
||||
5.391,
|
||||
506.723,
|
||||
383.367
|
||||
],
|
||||
"R": [
|
||||
-0.5269859333114162,
|
||||
-0.7554822816510317,
|
||||
-0.38927156099983334,
|
||||
0.6402732880654818,
|
||||
-0.05172298162073713,
|
||||
-0.7664038424760652,
|
||||
0.5588702377848656,
|
||||
-0.6531242265324508,
|
||||
0.5109724082909718
|
||||
],
|
||||
"exec_s": 2.96,
|
||||
"travel": 560.55,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 296,
|
||||
"sampled_points": 4489,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.19825289671655533,
|
||||
"collision_segments": 5,
|
||||
"first_collision_s": 5.597
|
||||
},
|
||||
{
|
||||
"i": 164,
|
||||
"task": {
|
||||
"tid": 1743026973,
|
||||
"id": "s0003_000814",
|
||||
"cat": "C",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-65.982,
|
||||
5.595,
|
||||
-64.669,
|
||||
-72.912,
|
||||
85.732,
|
||||
-86.377
|
||||
],
|
||||
"T": [
|
||||
-189.354,
|
||||
221.034,
|
||||
302.457
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.3000000000000003,
|
||||
"travel": 306.824,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 230,
|
||||
"sampled_points": 3865,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.12673094091895543,
|
||||
"collision_segments": 4,
|
||||
"first_collision_s": 3.0387500000000003
|
||||
},
|
||||
{
|
||||
"i": 175,
|
||||
"task": {
|
||||
"tid": 444406422,
|
||||
"id": "s0003_001100",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-28.854,
|
||||
43.33,
|
||||
-137.874,
|
||||
-6.895,
|
||||
-88.001,
|
||||
-43.745
|
||||
],
|
||||
"T": [
|
||||
379.105,
|
||||
293.88,
|
||||
653.541
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.58,
|
||||
"travel": 465.057,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 145,
|
||||
"sampled_points": 5769,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.954427674326439,
|
||||
"collision_segments": 2,
|
||||
"first_collision_s": 0.8236
|
||||
},
|
||||
{
|
||||
"i": 180,
|
||||
"task": {
|
||||
"tid": 737760595,
|
||||
"id": "s0003_001280",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
41.213,
|
||||
49.353,
|
||||
-149.808,
|
||||
-8.981,
|
||||
-88.566,
|
||||
83.45
|
||||
],
|
||||
"T": [
|
||||
118.145,
|
||||
2.733,
|
||||
266.082
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.78,
|
||||
"travel": 228.67,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 103,
|
||||
"sampled_points": 3385,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.1492959159660998,
|
||||
"collision_segments": 2,
|
||||
"first_collision_s": 1.0915789473684212
|
||||
},
|
||||
{
|
||||
"i": 197,
|
||||
"task": {
|
||||
"tid": 1433345185,
|
||||
"id": "s0004_000020",
|
||||
"cat": "B",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
20.112,
|
||||
-20.013,
|
||||
-177.85,
|
||||
32.746,
|
||||
-70.888,
|
||||
-30.111
|
||||
],
|
||||
"T": [
|
||||
-288.364,
|
||||
63.606,
|
||||
672.54
|
||||
],
|
||||
"R": [
|
||||
-0.5335375732231524,
|
||||
-0.7509294450860695,
|
||||
0.38915630081225366,
|
||||
-0.8443344656004893,
|
||||
0.4997538915602813,
|
||||
-0.19324947107169785,
|
||||
-0.049365657681080755,
|
||||
-0.4316839311036288,
|
||||
-0.9006730902323173
|
||||
],
|
||||
"exec_s": 2.14,
|
||||
"travel": 634.022,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 214,
|
||||
"sampled_points": 4317,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.0010684324191032601,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 215,
|
||||
"task": {
|
||||
"tid": 169730876,
|
||||
"id": "s0004_000544",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
47.051,
|
||||
-1.179,
|
||||
-94.31,
|
||||
-43.128,
|
||||
67.067,
|
||||
-119.867
|
||||
],
|
||||
"T": [
|
||||
-142.696,
|
||||
-139.159,
|
||||
726.163
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 2.16,
|
||||
"travel": 461.684,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 216,
|
||||
"sampled_points": 6341,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": -0.08817155079042394,
|
||||
"collision_segments": 3,
|
||||
"first_collision_s": 3.538888888888889
|
||||
},
|
||||
{
|
||||
"i": 224,
|
||||
"task": {
|
||||
"tid": 1884410379,
|
||||
"id": "s0004_000721",
|
||||
"cat": "A",
|
||||
"mode": "pose",
|
||||
"q0": [
|
||||
-127.447,
|
||||
7.845,
|
||||
-124.228,
|
||||
-20.968,
|
||||
-21.51,
|
||||
-35.505
|
||||
],
|
||||
"T": [
|
||||
-233.158,
|
||||
-59.78,
|
||||
648.987
|
||||
],
|
||||
"R": [
|
||||
0.3964618999442966,
|
||||
-0.020495209950317686,
|
||||
0.9178223729358808,
|
||||
-0.8179090164279511,
|
||||
0.4461640898622928,
|
||||
0.3632663564978371,
|
||||
-0.41694460392049726,
|
||||
-0.8947164641865291,
|
||||
0.1601238457417738
|
||||
],
|
||||
"exec_s": 1.48,
|
||||
"travel": 356.988,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 128,
|
||||
"sampled_points": 4707,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.0041216155493710716,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
},
|
||||
{
|
||||
"i": 255,
|
||||
"task": {
|
||||
"tid": 712677274,
|
||||
"id": "s0004_001686",
|
||||
"cat": "C",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
-65.548,
|
||||
71.865,
|
||||
27.462,
|
||||
-75.048,
|
||||
60.325,
|
||||
151.872
|
||||
],
|
||||
"T": [
|
||||
-53.154,
|
||||
50.204,
|
||||
397.489
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.84,
|
||||
"travel": 238.182,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"steps": 184,
|
||||
"sampled_points": 2495,
|
||||
"initial_gap64_mm": 0.5,
|
||||
"min_gap64_mm": 0.004616787795093558,
|
||||
"collision_segments": 0,
|
||||
"first_collision_s": null
|
||||
}
|
||||
],
|
||||
"audit_wall_s": 14.869853199998033
|
||||
}
|
||||
@@ -0,0 +1,71 @@
|
||||
{
|
||||
"task_index": 180,
|
||||
"task": {
|
||||
"tid": 737760595,
|
||||
"id": "s0003_001280",
|
||||
"cat": "B",
|
||||
"mode": "pos",
|
||||
"q0": [
|
||||
41.213,
|
||||
49.353,
|
||||
-149.808,
|
||||
-8.981,
|
||||
-88.566,
|
||||
83.45
|
||||
],
|
||||
"T": [
|
||||
118.145,
|
||||
2.733,
|
||||
266.082
|
||||
],
|
||||
"R": null,
|
||||
"exec_s": 1.78,
|
||||
"travel": 228.67,
|
||||
"reached": true,
|
||||
"err": 0,
|
||||
"split": "val"
|
||||
},
|
||||
"segment_start_it": 54,
|
||||
"start_s": 1.08,
|
||||
"end_s": 1.1,
|
||||
"original_discrete_col": 0,
|
||||
"certified": true,
|
||||
"recorded_endpoint_midpoint_gap32_mm": [
|
||||
0.0651998519897461,
|
||||
0.11720466613769531
|
||||
],
|
||||
"previous_midpoint_endpoint_gap64_mm": [
|
||||
0.5,
|
||||
0.11723191087430074,
|
||||
0.06521150921177998
|
||||
],
|
||||
"previous": [
|
||||
3.0118656158447266,
|
||||
-7.4926042556762695,
|
||||
-151.498291015625,
|
||||
-22.7498836517334,
|
||||
-94.0,
|
||||
93.50477600097656
|
||||
],
|
||||
"endpoint": [
|
||||
1.8212896585464478,
|
||||
-9.327469825744629,
|
||||
-150.87704467773438,
|
||||
-23.00735855102539,
|
||||
-94.0,
|
||||
93.73664855957031
|
||||
],
|
||||
"dense_max_step_deg": 0.01,
|
||||
"dense_points": 185,
|
||||
"min_gap64_mm": -0.14927288174659914,
|
||||
"min_at_fraction": 0.7663043478260869,
|
||||
"min_at_q": [
|
||||
2.09952208334985,
|
||||
-8.898669719696045,
|
||||
-151.0222272458284,
|
||||
-22.947187786516935,
|
||||
-94.0,
|
||||
93.68246095076852
|
||||
],
|
||||
"scope": "One executed segment; finite sampling demonstrates a collision between certified checkpoints."
|
||||
}
|
||||
@@ -551,6 +551,37 @@
|
||||
- 双精度运动学/间隙另与已有 JS 2000 个样本对拍：`python -m ml.eval.check_parity` PASS，碰撞布尔差 0、间隙最大差 2.53e−13 mm。首撞回放仍共用碰撞模型，不替代真机或独立连续认证。
|
||||
- **结论与下一步**：减少连续刹停确有小样本收益，但碰撞、行程和真实时延仍未过门槛；保留为实验选项，不更改查看器默认。下一轮记录原认证点与 J5 夹紧后实际点，修复后用同批同 seed A/B 和独立细采样复核，禁止靠关掉认证提速。
|
||||
|
||||
## 轮次 30：认证与执行统一逐步限位（exp: `exp/dagger-deploy`，局部修复有效，仍未零碰撞）
|
||||
|
||||
- **假设与判据**：安全层认证的是未夹紧累加轨迹，执行器却逐步夹紧 J5，导致认证通过后碰撞。必须同时证明原证书安全、执行点碰撞、记录命令经执行算式能准确重建实际点；修复后保持同任务、同种子、同模型、同增益和同排序，到达不下降，并用不依赖原 `col` 标记的加密回放检验。
|
||||
- **固定配方**：`python -u -m ml.eval.eval_cl --n 256 --commit 2 --fb 2.2 --partial 1 --replan empty --select endpoint --fb_block_gain 0.5 --dls_safety 1`；K=8、seed=0、analytic DLS、dag2 checkpoint、fallback=0，其余参数默认；分别附 `--out docs/experiment_round30_before.json --trace ml/runs/round30_before.pt --dump ml/runs/round30_before_rows.json` 和对应的 `after` 路径。未训练、未扩题、未改 JS 默认。
|
||||
- **可比性**：取证版 before 与轮次29首批256题完整轨迹逐元素完全相同（最大差 0°），ok/col/brakes/err/t_done/first_hit 也完全相同；before/after 配置仅输出路径不同。任务 SHA256=`249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772`，checkpoint SHA256=`f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1`，kin_fp=`2ebcbf17`。
|
||||
- **直接证据**（`docs/experiment_round30_certificates.json`）：旧版7次首撞的原认证端点/中点在 float32 与 float64 均为正间隙，实际执行段均有负间隙；`clamp(previous + recorded_command)` 与实际端点逐位一致。原证书与实际点主要差在 J5，首撞段差 0.340°–4.317°；其他轴仅有最高约 7.6e-6° 的累加舍入差，不足以解释毫米级穿入。全部有效认证步中，5298/34069 步偏差 >0.001°，最大 7.587°。
|
||||
- **修复范围**：`execution_points` 按执行器相同顺序逐步夹紧，端点和中点都由实际路径计算；整段、部分前缀和近目标 DLS 共用。r3 的旧队列终点和特征前缀改为真实执行结果。fallback 保持实验默认关闭，但两分支统一为“认证一步后补零”，不再把只验过一步的动作重复执行；端点和中点都认证。候选排序和反馈增益未改，0.5 仍只是间隙截顶，正间隙判据没有放宽。
|
||||
|
||||
| 指标（同256题） | 修复前 | 修复后 |
|
||||
|---|---|---|
|
||||
| 按时到达 | 147/256 = 57.4% | 同147题，57.4% |
|
||||
| 原端点/中点口径无碰撞到达 | 145/256 = 56.6% | 147/256 = 57.4%（下述加密审计否定其“全程无碰撞”含义） |
|
||||
| time_ok_1p3 | 39.1% | 39.1% |
|
||||
| 原端点/中点口径碰撞题 | 7/256 | 0/256 |
|
||||
| 成功题末端误差中位数 | 0.1951 mm | 0.1890 mm |
|
||||
| 成功题平均时间 | 2.2993 s | 2.3076 s |
|
||||
| 2 s 误差比中位数 | 0.2100 | 0.2100 |
|
||||
| 刹停/题 | 19.2383 | 19.2500 |
|
||||
| 成功题指令行程比中位数 | 2.4450 | 2.4450 |
|
||||
| 从实际轨迹重算成功题行程比 | 2.4251 | 2.4251 |
|
||||
| 整批同步仿真墙钟 | 247.515 s | 248.834 s |
|
||||
|
||||
- **执行一致性复核**（`docs/experiment_round30_comparison.json`）：修复后34107个有效认证步，认证端点/中点与实际执行点均逐位一致，最大差 0°；有效时段没有未经认证的移动步。这里“认证”仍仅指离散端点/中点，不能推出连续安全。CPU B=1、commit=2 的预测函数单独测量：100次预热、1000次采样、单线程，P50 0.0388→0.0518 ms，P99 0.0691→0.0939 ms；不是完整规划延迟，不能证明50 Hz。
|
||||
- **独立加密审计的正反对照**：预先固定12题 `0,21,84,128,145,164,175,180,197,215,224,255`，其中包含全部7题旧首撞及5题其他任务；不按新版 `col` 过滤。逐题回放至自身时限或到达，含起点，每个插值段最大轴步距≤0.05°并包含中点，float64、每批64点。命令：`python -u -m ml.eval.diag_collision --trace ml/runs/round30_before.pt --dense_indices 0,21,84,128,145,164,175,180,197,215,224,255 --max_step_deg 0.05 --device cuda --out docs/experiment_round30_dense_before.json`，after 同理；串行运行。
|
||||
- before：56118点，**7/12题检出碰撞**，14.870 s；证明探针确实能抓旧碰撞。
|
||||
- after：56566点，**仍有1/12题碰撞**，15.057 s；其余11题采样点间隙为正。这是小样本有限采样，不是完整256题的连续认证。
|
||||
- 剩余第180题，执行段1.08–1.10 s：上一点/中点/端点 float64 间隙为 **0.5 / 0.11723 / 0.06521 mm**，都安全，但段内约76.6%处为 **−0.14927 mm**；该段0.01°再加密185点仍检出，见 `docs/experiment_round30_missed_segment.json`。因此这次残余是端点/中点漏检，而非认证与执行错位；它比旧离散首次碰撞1.14 s还早。不得把内部计数0当成修好了；已知真实无碰撞到达至多146/256=57.0%，其余未加密任务仍未知。
|
||||
- **回归与判据灵敏度**：`python -m unittest tests.test_eval_cl -v` 14项通过。覆盖整段/部分限位拒绝、只保留真实安全前缀、DLS被拒后保留候选、r3旧队列起点和特征前缀及逐点证书、fallback单步/中点拒绝。把预测函数在内存中替回旧算式，限位、DLS和r3测试重新失败。加密探针另有合成正对照：原端点/中点全安全且 `col=0`，仍检出四分之一处薄障碍；安全负对照、截止后/到达后碰撞排除均通过。
|
||||
- **新增硬约束证据**：before/after 两组256题实际轨迹都出现超速，最大 **1.660004×VMAX**（来源是动作限幅后叠加0.3×gain2.2反馈）。实际各轴最大速度约 `[99.600,99.600,149.400,149.400,199.200,298.800]°/s`，并非只看原始指令推测。行程仍约参考2.425倍；速度、加速度和真实控制时延均未达部署验收。历史 `travel` 累积的是指令而非夹紧后位移，本轮已同时列出实际轨迹重算值，不悄悄换分母。
|
||||
- **结论与下一步**：逐步限位一致性修复有效，未牺牲本批到达率；但加密审计仍检出段内碰撞，**不能部署、不能宣称零碰撞或50 Hz合格**。下一轮先验证更强的段内认证，再单独评估反馈后的速度/加速度约束与真实端到端调度；每次保持小样本单变量对照，继续保留失败证据。
|
||||
|
||||
## 分支清理（2026-10-20）
|
||||
|
||||
- `feat/fb-mix`（8e5431c）：反馈混合方案的中间分支，**已验证是 `exp/dagger-deploy` 的祖先**（所有提交都在后者历史里），本地/远端分支删除，内容保留在 exp/dagger-deploy。
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
口径与 closed_loop.rollout 一致：每步同时检查当前状态 q 与相邻状态中点 mid 的间隙（cap=0.5 mm）；
|
||||
间隙组对索引 = gA*7+gB（gA<gB，g 为连杆组 0..5 → J1..J6）。
|
||||
"""
|
||||
import argparse, json, math, os, sys
|
||||
import argparse, json, math, os, sys, time
|
||||
from collections import Counter
|
||||
|
||||
import torch
|
||||
@@ -54,6 +54,25 @@ def replay_first_hits(path, out=None):
|
||||
'previous': prev.tolist(), 'endpoint': q.tolist(),
|
||||
'dense_nsegments': nseg, 'dense_gap64_min_mm': float(dense_gap.min()),
|
||||
}
|
||||
if 'plans' in per:
|
||||
plans = per['plans']
|
||||
pi = int(torch.searchsorted(plans['it'], it - 1, right=True)) - 1
|
||||
step = it - 1 - int(plans['it'][pi])
|
||||
ncommit = plans['commit'].shape[2]
|
||||
command = plans['commit'][b, pi, step]
|
||||
cp, cm = plans['points'][b, pi, step], plans['midpoints'][b, pi, step]
|
||||
checked = bool(plans['certified'][b, pi, step])
|
||||
predicted = k32.clamp_box((prev + command)[None])[0]
|
||||
row['certificate'] = {
|
||||
'plan_start_it': int(plans['it'][pi]), 'step': step, 'checked': checked,
|
||||
'endpoint': cp.tolist() if checked else None,
|
||||
'midpoint': cm.tolist() if checked else None,
|
||||
'recorded_gap32_mm': plans['gaps'][b, pi, [step, ncommit + step]].tolist() if checked else None,
|
||||
'replayed_gap64_mm': k64.gaps(torch.stack([cp, cm]).double(), 0.5)['gm'].min(-1).values.tolist() if checked else None,
|
||||
'command_delta': command.tolist(),
|
||||
'clamped_reconstruction_max_abs_deg': float((predicted - q).abs().max()),
|
||||
'certified_vs_executed_max_abs_deg': float((cp - q).abs().max()) if checked else None,
|
||||
}
|
||||
rows.append(row)
|
||||
print('task %d first_hit %.2fs gap32 %s gap64 %s boundary %s' %
|
||||
(row['i'], row['first_hit_s'], row['gap32_mm'], row['gap64_mm'], row['boundary_joints']), flush=True)
|
||||
@@ -73,10 +92,73 @@ def replay_first_hits(path, out=None):
|
||||
return result
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def replay_dense(path, indices, max_step_deg=0.05, device='cpu', out=None):
|
||||
saved = torch.load(path, map_location='cpu', weights_only=True)
|
||||
kin = Kin(device=device, dtype=torch.float64)
|
||||
if saved.get('kin_fp') != kin.fp:
|
||||
raise ValueError('trace/kin fingerprint mismatch')
|
||||
if not indices or min(indices) < 0 or max(indices) >= len(saved['tasks']) or max_step_deg <= 0:
|
||||
raise ValueError('valid task indices and positive max_step_deg required')
|
||||
started = time.perf_counter()
|
||||
rows, offset = [], 0
|
||||
for _, per, count in saved['batches']:
|
||||
for index in indices:
|
||||
if not offset <= index < offset + count:
|
||||
continue
|
||||
b = index - offset
|
||||
nstep = min(per['traj'].shape[1] - 1, math.floor((float(per['limit'][b]) + 1e-6) / DT))
|
||||
if torch.isfinite(per['t_done'][b]):
|
||||
nstep = min(nstep, round(float(per['t_done'][b]) / DT))
|
||||
trajectory = per['traj'][b, :nstep + 1].double()
|
||||
points, segment_ids, fractions = [trajectory[:1]], [torch.tensor([-1])], [torch.zeros(1)]
|
||||
for it in range(nstep):
|
||||
prev, end = trajectory[it:it + 2]
|
||||
nseg = max(2, 2 * math.ceil(float((end - prev).abs().max()) / (2 * max_step_deg)))
|
||||
alpha = torch.arange(1, nseg + 1, dtype=torch.float64) / nseg
|
||||
points.append(prev + alpha[:, None] * (end - prev))
|
||||
segment_ids.append(torch.full((nseg,), it))
|
||||
fractions.append(alpha)
|
||||
points = torch.cat(points)
|
||||
segments, alpha = torch.cat(segment_ids), torch.cat(fractions)
|
||||
print('dense task %d steps %d points %d start' % (index, nstep, len(points)), flush=True)
|
||||
gaps = []
|
||||
for start in range(0, len(points), 64):
|
||||
gaps.append(kin.gaps(points[start:start + 64].to(device), 0.5)['gm'].min(-1).values.cpu())
|
||||
if (start // 64 + 1) % 64 == 0:
|
||||
print('dense task %d points %d/%d wall %.1fs' %
|
||||
(index, min(start + 64, len(points)), len(points), time.perf_counter() - started), flush=True)
|
||||
gaps = torch.cat(gaps)
|
||||
hit = gaps <= 0
|
||||
first = int(hit.nonzero()[0]) if hit.any() else None
|
||||
rows.append({
|
||||
'i': index, 'task': saved['tasks'][index], 'steps': nstep, 'sampled_points': len(points),
|
||||
'initial_gap64_mm': float(gaps[0]), 'min_gap64_mm': float(gaps.min()),
|
||||
'collision_segments': int(segments[hit & (segments >= 0)].unique().numel()),
|
||||
'first_collision_s': max(0.0, float(segments[first] + alpha[first]) * DT) if first is not None else None,
|
||||
})
|
||||
print('dense task %d min_gap %.9f mm collision_segments %d' %
|
||||
(index, rows[-1]['min_gap64_mm'], rows[-1]['collision_segments']), flush=True)
|
||||
offset += count
|
||||
result = {
|
||||
'trace': str(path), 'tasks_sha256': saved.get('tasks_sha256'), 'kin_fp': kin.fp,
|
||||
'indices': indices, 'max_step_deg': max_step_deg, 'dtype': 'float64', 'device': device,
|
||||
'scope': 'Executed trajectory through each task deadline or arrival, including initial state; finite sampling, not continuous certification.',
|
||||
'collision_tasks': sum(r['min_gap64_mm'] <= 0 for r in rows), 'rows': rows,
|
||||
'audit_wall_s': time.perf_counter() - started,
|
||||
}
|
||||
if out:
|
||||
with open(out, 'w', encoding='utf-8') as f:
|
||||
json.dump(result, f, indent=2, allow_nan=False)
|
||||
return result
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument('--ckpt')
|
||||
ap.add_argument('--trace')
|
||||
ap.add_argument('--dense_indices', help='逗号分隔的任务下标；回放实际轨迹，不依赖原 col 标记')
|
||||
ap.add_argument('--max_step_deg', type=float, default=0.05)
|
||||
ap.add_argument('--samples', default='ml/data/samples')
|
||||
ap.add_argument('--split', default='val')
|
||||
ap.add_argument('--n', type=int, default=512)
|
||||
@@ -88,8 +170,13 @@ def main():
|
||||
ap.add_argument('--out', default=None)
|
||||
a = ap.parse_args()
|
||||
if a.trace:
|
||||
replay_first_hits(a.trace, a.out)
|
||||
if a.dense_indices:
|
||||
replay_dense(a.trace, [int(i) for i in a.dense_indices.split(',')], a.max_step_deg, a.device, a.out)
|
||||
else:
|
||||
replay_first_hits(a.trace, a.out)
|
||||
return
|
||||
if a.dense_indices:
|
||||
ap.error('--dense_indices requires --trace')
|
||||
if not a.ckpt:
|
||||
ap.error('--ckpt or --trace is required')
|
||||
|
||||
|
||||
+1
-1
@@ -20,7 +20,7 @@ def run(model, kin, tasks, device, bs=256, **kw):
|
||||
r, p = rollout(model, kin, tasks[i:i + bs], device, progress=progress, **kw)
|
||||
per.append((r, p, len(tasks[i:i + bs])))
|
||||
print('batch %d done wall %.1fs' % (i // bs + 1, time.perf_counter() - t0), flush=True)
|
||||
flat = {k: torch.cat([p[k] for _, p, _ in per]) for k in per[0][1] if k != 'traj'}
|
||||
flat = {k: torch.cat([p[k] for _, p, _ in per]) for k in per[0][1] if k not in ('traj', 'plans')}
|
||||
_, _, _, ex, tr = tasks_to_tensors(tasks, 'cpu')
|
||||
ok, col = flat['ok'], flat['col'] > 0
|
||||
has_ref = torch.isfinite(ex)
|
||||
|
||||
+69
-25
@@ -48,6 +48,16 @@ def goal_err(kin, q, T, R):
|
||||
return e, oe
|
||||
|
||||
|
||||
def execution_points(kin, q, increments):
|
||||
points, midpoints = [], []
|
||||
for delta in increments.unbind(1):
|
||||
nxt = kin.clamp_box(q + delta)
|
||||
midpoints.append(q + 0.5 * (nxt - q))
|
||||
points.append(nxt)
|
||||
q = nxt
|
||||
return torch.stack(points, 1), torch.stack(midpoints, 1)
|
||||
|
||||
|
||||
def dls_steps(kin, q, T, R, n=3, scale=0.3, lam=0.5, analytic=True):
|
||||
"""局部 DLS 精修：n 步增量 [B,n,6]（analytic：几何雅可比，1 次 FK；否则 7 次批量有限差分）"""
|
||||
step = torch.tensor(STEP, device=q.device)
|
||||
@@ -181,6 +191,7 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
col = torch.zeros(B, device=device); travel = torch.zeros(B, device=device); brakes = torch.zeros(B, device=device)
|
||||
first_hit = torch.full((B,), float('nan'), device=device); dls_it = torch.full((B,), -1, dtype=torch.long, device=device)
|
||||
traj = [q.clone()] if return_traj else None
|
||||
plans = {k: [] for k in ('it', 'qe', 'commit', 'points', 'midpoints', 'gaps', 'certified')} if return_traj else None
|
||||
n_start = int(round(0.5 / DT)); moved_start = None # R2 起步失败：0.5 s 内关节总行程 < 1°
|
||||
k2s = int(round(2.0 / DT)); e2 = None
|
||||
e0, _ = goal_err(kin, q0, T, R)
|
||||
@@ -192,8 +203,12 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
tnow = it * DT
|
||||
if (queue.shape[1] <= 3) if replan == 'r3' else (queue.shape[1] == 0):
|
||||
r = queue.shape[1]
|
||||
pre = torch.zeros(B, 3, 6, device=device); pre[:, :r] = queue[:, :r]
|
||||
qe = q + queue.sum(1)
|
||||
pre = torch.zeros(B, 3, 6, device=device)
|
||||
qe = q
|
||||
if r:
|
||||
prefix_points, _ = execution_points(kin, q, queue)
|
||||
pre[:, :r] = prefix_points - torch.cat([q[:, None], prefix_points[:, :-1]], 1)
|
||||
qe = prefix_points[:, -1]
|
||||
e, oe = goal_err(kin, qe, T, R)
|
||||
near = (e < 5) & ((R.abs().sum(-1) == 0) | (oe < 5))
|
||||
subs, nsub, sub_prev, sub_t = sub_advance(kin, subs, nsub, sub_prev, sub_t, qe, tnow, sub_rule)
|
||||
@@ -297,31 +312,34 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
collect.append((feat.detach().float().cpu(), inc[ar, pick].detach().float().cpu()))
|
||||
committed = torch.ones(B, dtype=torch.bool, device=device)
|
||||
braked = torch.zeros(B, dtype=torch.bool, device=device)
|
||||
if return_traj:
|
||||
cert_pts = torch.full((B, commit_n, 6), float('nan'), device=device)
|
||||
cert_mids = cert_pts.clone()
|
||||
cert_gaps = torch.full((B, 2 * commit_n), float('nan'), device=device)
|
||||
certified = torch.zeros(B, commit_n, dtype=torch.bool, device=device)
|
||||
if safety and safety_partial: # 2026-10-20：允许「部分安全前缀」——执行到第一个不安全步为止，而不是全拒刹停
|
||||
maxlen = torch.zeros(B, dtype=torch.long, device=device)
|
||||
chosen = torch.zeros(B, commit_n, 6, device=device); chosen_k = pick.clone()
|
||||
ntry = min(K_eff, safety_try) if safety_try > 0 else K_eff
|
||||
for kk in range(ntry):
|
||||
ck = inc[ar, order[:, kk], r:r + commit_n]
|
||||
pts = qe[:, None] + torch.cumsum(ck, 1)
|
||||
mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ck
|
||||
pts, mids = execution_points(kin, qe, ck)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
ok6 = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 2 * commit_n)
|
||||
okstep = ok6[:, :commit_n] & ok6[:, commit_n:] # 每步：步点与其中点都安全
|
||||
plen = torch.cumprod(okstep.long(), 1).sum(1) # 安全前缀长度 0..3
|
||||
gaps6 = kin.gaps(allq, 0.5)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
ok6 = gaps6 > 0
|
||||
okstep = ok6[:, :commit_n] & ok6[:, commit_n:]
|
||||
plen = torch.cumprod(okstep.long(), 1).sum(1)
|
||||
take = plen > maxlen
|
||||
chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k)
|
||||
if return_traj:
|
||||
cert_pts = torch.where(take[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(take[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
|
||||
maxlen = torch.maximum(maxlen, plen)
|
||||
if B == 1 and int(maxlen[0]) >= commit_n: break # 实时单题：首个全安全候选即停（安全层 16 点→2-4 点）
|
||||
msk = (torch.arange(commit_n, device=device)[None] < maxlen[:, None])
|
||||
commit = torch.where(msk[..., None], chosen, torch.zeros_like(chosen))
|
||||
if fb_gain > 0 and fb_fallback > 0: # 全拒时兜底：半步朝目标（须通过间隙认证），避免原地停满整个周期
|
||||
fb1 = dls_steps(kin, qe, T, R, n=1) * fb_fallback
|
||||
ok1 = kin.gaps(qe + fb1[:, 0], 0.5)['gm'].min(-1).values > 0
|
||||
fbcom = torch.zeros(B, commit_n, 6, device=device); fbcom[:, 0] = fb1[:, 0] # 只走一步（重复会被第二步未经认证的位移带进碰撞）
|
||||
mskf = (maxlen == 0) & ok1
|
||||
commit = torch.where(mskf[:, None, None], fbcom, commit)
|
||||
msk = msk | mskf[:, None]
|
||||
if return_traj: certified = msk.clone()
|
||||
brakes += (maxlen == 0).float(); braked = maxlen == 0
|
||||
pick = chosen_k; committed = maxlen > 0
|
||||
elif safety:
|
||||
@@ -330,28 +348,48 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
ntry = min(K_eff, safety_try) if safety_try > 0 else K_eff
|
||||
for kk in range(ntry):
|
||||
ck = inc[ar, order[:, kk], r:r + commit_n]
|
||||
pts = qe[:, None] + torch.cumsum(ck, 1)
|
||||
mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ck
|
||||
pts, mids = execution_points(kin, qe, ck)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
okk = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 2 * commit_n).all(-1)
|
||||
gaps6 = kin.gaps(allq, 0.5)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
okk = (gaps6 > 0).all(-1)
|
||||
take = okk & ~ok_any
|
||||
chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k); ok_any |= okk
|
||||
if return_traj:
|
||||
cert_pts = torch.where(take[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(take[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
|
||||
commit = torch.where(ok_any[:, None, None], chosen, torch.zeros_like(chosen))
|
||||
if fb_gain > 0 and fb_fallback > 0: # 全拒时的兜底：半速朝目标走一步（须通过安全认证），避免原地停滞
|
||||
fb1 = dls_steps(kin, qe, T, R, n=1) * fb_fallback
|
||||
ok1 = kin.gaps(qe + fb1[:, 0], 0.5)['gm'].min(-1).values > 0
|
||||
commit = torch.where((~ok_any & ok1)[:, None, None], fb1.expand(-1, commit_n, -1), commit)
|
||||
if return_traj: certified = ok_any[:, None].expand(-1, commit_n).clone()
|
||||
brakes += (~ok_any).float(); braked = ~ok_any
|
||||
pick = chosen_k; committed = ok_any
|
||||
if safety and fb_gain > 0 and fb_fallback > 0:
|
||||
fbcom = torch.zeros(B, commit_n, 6, device=device)
|
||||
fbcom[:, :1] = dls_steps(kin, qe, T, R, n=1) * fb_fallback
|
||||
pts, mids = execution_points(kin, qe, fbcom)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
gaps6 = kin.gaps(allq, 0.5)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
takef = braked & (gaps6 > 0).all(-1)
|
||||
commit = torch.where(takef[:, None, None], fbcom, commit)
|
||||
if return_traj:
|
||||
cert_pts = torch.where(takef[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(takef[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(takef[:, None], gaps6, cert_gaps)
|
||||
certified = certified | takef[:, None]
|
||||
if near.any():
|
||||
ref = dls_steps(kin, qe, T, R, n=commit_n)
|
||||
take = near
|
||||
if dls_safety and safety: # 2026-10-20：近目标 DLS 接管也要过安全层（部署端 _certCommit 本来就会验；不过则退回候选计划）
|
||||
pts = qe[:, None] + torch.cumsum(ref, 1)
|
||||
mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ref
|
||||
pts, mids = execution_points(kin, qe, ref)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
okd = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 2 * commit_n).all(-1)
|
||||
gaps6 = kin.gaps(allq, 0.5)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
okd = (gaps6 > 0).all(-1)
|
||||
take = near & okd
|
||||
if return_traj:
|
||||
cert_pts = torch.where(take[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(take[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
|
||||
if return_traj:
|
||||
certified = torch.where(take[:, None], torch.full_like(certified, bool(dls_safety and safety)), certified)
|
||||
commit = torch.where(take[:, None, None], ref, commit)
|
||||
dls_it = torch.where(take & (dls_it < 0), torch.full_like(dls_it, it), dls_it)
|
||||
committed = committed & ~take
|
||||
@@ -359,6 +397,10 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
if fb_ki > 0: # 刹停/精修接管时清积分（防止原地累积到限幅后突然冲出去）
|
||||
fb_i = torch.where(committed[:, None], fb_i, torch.zeros_like(fb_i))
|
||||
last = torch.cat([qe[:, None], fut[ar, pick]], 1); last_ok = committed; last_brake = braked
|
||||
if return_traj:
|
||||
plans['it'].append(it + r)
|
||||
for key, value in (('qe', qe), ('commit', commit), ('points', cert_pts), ('midpoints', cert_mids), ('gaps', cert_gaps), ('certified', certified)):
|
||||
plans[key].append(value)
|
||||
queue = torch.cat([queue, commit], 1)
|
||||
cold = False
|
||||
d = queue[:, 0]; queue = queue[:, 1:]
|
||||
@@ -403,7 +445,9 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
per = {'done': done.cpu(), 'ok': ok.cpu(), 't_done': t_done.cpu(), 'limit': limit.cpu(), 'col': col.cpu(), 'travel': travel.cpu(), 'err': e_end.cpu(),
|
||||
'side': side.cpu(), 'start_fail': start_fail.cpu(), 'brakes': brakes.cpu(), 'first_hit': first_hit.cpu(), 'dls_it': dls_it.cpu(),
|
||||
'goal_ratio_2s': ratio2.cpu()}
|
||||
if return_traj: per['traj'] = torch.stack(traj, 1).cpu()
|
||||
if return_traj:
|
||||
per['traj'] = torch.stack(traj, 1).cpu()
|
||||
per['plans'] = {k: torch.tensor(v) if k == 'it' else torch.stack(v, 1).cpu() for k, v in plans.items()}
|
||||
return res, per
|
||||
|
||||
|
||||
|
||||
+216
-1
@@ -9,7 +9,7 @@ from unittest.mock import patch
|
||||
import torch
|
||||
|
||||
from ml.eval.eval_cl import json_finite, run
|
||||
from ml.eval.diag_collision import replay_first_hits
|
||||
from ml.eval.diag_collision import replay_dense, replay_first_hits
|
||||
from ml.sim.closed_loop import TeacherOracle, rollout
|
||||
|
||||
|
||||
@@ -91,6 +91,39 @@ class EvalMetricsTest(unittest.TestCase):
|
||||
self.assertEqual(result['rows'][0]['gap64_mm'], [0.5, 0.5, 0.0, -1.0])
|
||||
self.assertEqual(result['rows'][0]['dense_gap64_min_mm'], -1.0)
|
||||
|
||||
def test_dense_probe_detects_between_checkpoints_and_respects_deadline(self):
|
||||
class ThinObstacle:
|
||||
fp = 'probe'
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
pass
|
||||
|
||||
def gaps(self, q, cap):
|
||||
return {'gm': ((q[:, :1] - 0.25).abs() - 0.05).clamp(max=cap)}
|
||||
|
||||
traj = torch.zeros(4, 3, 6)
|
||||
traj[:, :, 0] = torch.tensor([[0, 1, 1], [0, -0.5, -1], [0, 0, 0.25], [0, 0, 0.25]])
|
||||
per = {'traj': traj, 'col': torch.zeros(4), 'limit': torch.tensor([0.04, 0.04, 0.02, 0.04]),
|
||||
't_done': torch.tensor([float('nan')] * 3 + [0.02])}
|
||||
with tempfile.TemporaryDirectory() as folder:
|
||||
trace = Path(folder) / 'trace.pt'
|
||||
output = Path(folder) / 'audit.json'
|
||||
torch.save({'kin_fp': 'probe', 'tasks': [{}] * 4, 'batches': [({}, per, 4)]}, trace)
|
||||
with patch('ml.eval.diag_collision.Kin', ThinObstacle), contextlib.redirect_stdout(io.StringIO()):
|
||||
result = replay_dense(trace, [0, 1, 2, 3], 0.01, out=output)
|
||||
with self.assertRaisesRegex(ValueError, 'valid task indices'):
|
||||
replay_dense(trace, [4])
|
||||
with self.assertRaisesRegex(ValueError, 'positive max_step_deg'):
|
||||
replay_dense(trace, [0], 0.0)
|
||||
self.assertEqual(json.loads(output.read_text())['collision_tasks'], 1)
|
||||
self.assertEqual(result['collision_tasks'], 1)
|
||||
self.assertEqual(result['rows'][0]['collision_segments'], 1)
|
||||
self.assertAlmostEqual(result['rows'][0]['min_gap64_mm'], -0.05)
|
||||
self.assertGreater(result['rows'][1]['min_gap64_mm'], 0)
|
||||
for row in result['rows'][2:]:
|
||||
self.assertGreater(row['min_gap64_mm'], 0)
|
||||
self.assertEqual(row['steps'], 1)
|
||||
|
||||
|
||||
class DlsSafetyTest(unittest.TestCase):
|
||||
def test_rejected_refinement_retains_safe_candidate(self):
|
||||
@@ -158,5 +191,187 @@ class DlsSafetyTest(unittest.TestCase):
|
||||
self.assertEqual(results[1]['col'].item(), 0.0)
|
||||
|
||||
|
||||
class ClampedPathSafetyTest(unittest.TestCase):
|
||||
def rollout_candidate(self, increments, safety_partial, goal_error=10.0, **kwargs):
|
||||
class ObstacleKin:
|
||||
def clamp_box(self, q):
|
||||
clamped = q.clone()
|
||||
clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
|
||||
return clamped
|
||||
|
||||
def gaps(self, q, cap):
|
||||
collision = (q[:, :1] > 0.1) & (q[:, 4:5] < 0.2)
|
||||
return {'gm': torch.where(collision, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||||
|
||||
task = {'q0': [0] * 6, 'T': [goal_error, 0, 0]}
|
||||
commit_n = len(increments)
|
||||
traj = torch.zeros(1, commit_n + 1, 6)
|
||||
traj[0, 1:] = torch.tensor(increments).cumsum(0)
|
||||
patches = {
|
||||
'features': lambda *args: torch.zeros(1, 83),
|
||||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), goal_error), torch.zeros(q.shape[0])),
|
||||
}
|
||||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||||
_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
|
||||
max_sec=0.02 * commit_n, return_traj=True, use_sub=False, select='first',
|
||||
oracle=TeacherOracle(traj), replan='empty', commit_n=commit_n,
|
||||
safety_partial=safety_partial, **kwargs)
|
||||
return per
|
||||
|
||||
def test_full_safety_rejects_collision_after_joint_clamp(self):
|
||||
# Raw endpoint/midpoint are safe, but the J5 upper limit puts the endpoint at (0.2, 0).
|
||||
per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=0)
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
|
||||
|
||||
def test_partial_safety_rejects_collision_after_joint_clamp(self):
|
||||
per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=1)
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
|
||||
|
||||
def test_partial_safety_keeps_only_clamped_safe_prefix(self):
|
||||
per = self.rollout_candidate([
|
||||
[0.05, 0, 0, 0, 0.6, 0],
|
||||
[0.2, 0, 0, 0, 0.6, 0],
|
||||
], safety_partial=1)
|
||||
self.assertAlmostEqual(per['traj'][0, -1, 0].item(), 0.05)
|
||||
self.assertEqual(per['traj'][0, -1, 4].item(), 0.0)
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
expected = torch.zeros(1, 3, 6)
|
||||
expected[0, 1:, 0] = 0.05
|
||||
torch.testing.assert_close(per['traj'], expected)
|
||||
torch.testing.assert_close(per['plans']['commit'][0, 0, 1], torch.zeros(6), rtol=0, atol=0)
|
||||
|
||||
def test_near_dls_rejects_clamped_collision_and_keeps_candidate(self):
|
||||
candidate = [[0.05, 0, 0, 0, 0.6, 0]]
|
||||
ref = torch.tensor([[[0.2, 0, 0, 0, 0.6, 0]]])
|
||||
for safety_partial in (0, 1):
|
||||
with self.subTest(safety_partial=safety_partial):
|
||||
with patch('ml.sim.closed_loop.dls_steps', return_value=ref) as dls:
|
||||
per = self.rollout_candidate(candidate, safety_partial, goal_error=2.0, dls_safety=1)
|
||||
dls.assert_called_once()
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
self.assertEqual(per['dls_it'].item(), -1)
|
||||
self.assertEqual(per['brakes'].item(), 0.0)
|
||||
expected = torch.zeros(1, 2, 6)
|
||||
expected[0, 1, 0] = 0.05
|
||||
torch.testing.assert_close(per['traj'], expected)
|
||||
torch.testing.assert_close(per['plans']['commit'], torch.tensor(candidate)[None, None])
|
||||
self.assertTrue(per['plans']['certified'].all().item())
|
||||
|
||||
def test_r3_clamped_queue_origin_prefix_and_certificates_match_execution(self):
|
||||
class SafeKin:
|
||||
def clamp_box(self, q):
|
||||
clamped = q.clone()
|
||||
clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
|
||||
return clamped
|
||||
|
||||
def gaps(self, q, cap):
|
||||
return {'gm': torch.ones_like(q[:, :1])}
|
||||
|
||||
prefixes = []
|
||||
|
||||
def capture_features(kin, q, qp, T, R, pre, r, sub, sf):
|
||||
prefixes.append((pre.clone(), int(r.item())))
|
||||
return torch.zeros(1, 83)
|
||||
|
||||
teacher = torch.zeros(1, 25, 6)
|
||||
teacher[0, :, 0] = 0.02 * torch.arange(25)
|
||||
teacher[0, :, 4] = 0.6 * torch.arange(25)
|
||||
task = {'q0': [0] * 6, 'T': [10, 0, 0]}
|
||||
patches = {
|
||||
'features': capture_features,
|
||||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
|
||||
}
|
||||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||||
_, per = rollout(None, SafeKin(), [task], 'cpu', K=1, safety=True,
|
||||
max_sec=0.16, return_traj=True, use_sub=False, select='first',
|
||||
oracle=TeacherOracle(teacher), replan='r3', commit_n=3, safety_partial=1)
|
||||
self.assertEqual([r for _, r in prefixes], [0, 2, 3, 3])
|
||||
for pre, r in prefixes[1:]:
|
||||
torch.testing.assert_close(pre[..., 4], torch.zeros(1, 3), rtol=0, atol=0)
|
||||
torch.testing.assert_close(pre[:, :r, 0], torch.full((1, r), 0.02))
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
expected = torch.zeros(1, 9, 6)
|
||||
expected[0, :, 0] = 0.02 * torch.arange(9)
|
||||
torch.testing.assert_close(per['traj'], expected)
|
||||
plans = per['plans']
|
||||
self.assertEqual(plans['it'].tolist(), [0, 3, 6, 9])
|
||||
torch.testing.assert_close(plans['commit'][..., 4], torch.full((1, 4, 3), 0.6))
|
||||
self.assertTrue(plans['certified'].all().item())
|
||||
self.assertTrue((plans['gaps'] > 0).all().item())
|
||||
checked = 0
|
||||
for i, start in enumerate(plans['it'].tolist()):
|
||||
n = min(3, 8 - start)
|
||||
if n <= 0:
|
||||
continue
|
||||
torch.testing.assert_close(plans['qe'][:, i], per['traj'][:, start], rtol=0, atol=0)
|
||||
before = per['traj'][:, start:start + n]
|
||||
after = per['traj'][:, start + 1:start + n + 1]
|
||||
torch.testing.assert_close(plans['points'][:, i, :n], after, rtol=0, atol=0)
|
||||
torch.testing.assert_close(plans['midpoints'][:, i, :n], before + 0.5 * (after - before),
|
||||
rtol=0, atol=0)
|
||||
checked += n
|
||||
self.assertEqual(checked, 8)
|
||||
|
||||
def rollout_fallback(self, safety_partial, midpoint_collision=False):
|
||||
class ObstacleKin:
|
||||
def clamp_box(self, q):
|
||||
return q
|
||||
|
||||
def gaps(self, q, cap):
|
||||
collision = q[:, :1] > 0.3
|
||||
if midpoint_collision:
|
||||
collision |= (q[:, :1] > 0.09) & (q[:, :1] < 0.11)
|
||||
return {'gm': torch.where(collision, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||||
|
||||
teacher = torch.zeros(1, 4, 6)
|
||||
teacher[0, :, 0] = torch.arange(4)
|
||||
ref = torch.zeros(1, 1, 6)
|
||||
ref[0, 0, 0] = 0.2
|
||||
task = {'q0': [0] * 6, 'T': [10, 0, 0]}
|
||||
patches = {
|
||||
'features': lambda *args: torch.zeros(1, 83),
|
||||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
|
||||
'dls_steps': lambda *args, **kwargs: ref,
|
||||
}
|
||||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||||
_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
|
||||
max_sec=0.06, return_traj=True, use_sub=False, select='first',
|
||||
oracle=TeacherOracle(teacher), replan='empty', commit_n=3,
|
||||
safety_partial=safety_partial, fb_gain=1.0, fb_fallback=1.0)
|
||||
return per
|
||||
|
||||
def test_fallback_executes_one_safe_dls_step_then_certified_zeros(self):
|
||||
for safety_partial in (0, 1):
|
||||
with self.subTest(safety_partial=safety_partial):
|
||||
per = self.rollout_fallback(safety_partial)
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
self.assertEqual(per['brakes'].item(), 1.0)
|
||||
expected = torch.zeros(1, 4, 6)
|
||||
expected[0, 1:, 0] = 0.2
|
||||
torch.testing.assert_close(per['traj'], expected)
|
||||
commit = torch.zeros(1, 1, 3, 6)
|
||||
commit[0, 0, 0, 0] = 0.2
|
||||
plans = per['plans']
|
||||
self.assertEqual(plans['it'].tolist(), [0])
|
||||
torch.testing.assert_close(plans['commit'], commit, rtol=0, atol=0)
|
||||
self.assertTrue(plans['certified'].all().item())
|
||||
self.assertTrue((plans['gaps'] > 0).all().item())
|
||||
torch.testing.assert_close(plans['points'][:, 0], expected[:, 1:])
|
||||
torch.testing.assert_close(plans['midpoints'][:, 0],
|
||||
expected[:, :-1] + 0.5 * (expected[:, 1:] - expected[:, :-1]))
|
||||
|
||||
def test_fallback_rejects_safe_endpoint_with_colliding_midpoint(self):
|
||||
for safety_partial in (0, 1):
|
||||
with self.subTest(safety_partial=safety_partial):
|
||||
per = self.rollout_fallback(safety_partial, midpoint_collision=True)
|
||||
self.assertEqual(per['col'].item(), 0.0)
|
||||
self.assertEqual(per['brakes'].item(), 1.0)
|
||||
torch.testing.assert_close(per['traj'], torch.zeros(1, 4, 6), rtol=0, atol=0)
|
||||
torch.testing.assert_close(per['plans']['commit'], torch.zeros(1, 1, 3, 6), rtol=0, atol=0)
|
||||
self.assertFalse(per['plans']['certified'].any().item())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user