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

2026-10-20 17:26:20 +08:00
parent 9eaff7341f
commit 95da49193f
12 changed files with 2516 additions and 29 deletions
@@ -0,0 +1,62 @@
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+}
@@ -0,0 +1,485 @@
+{
+  "trace": "ml/runs/round30_before.pt",
+  "tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
+  "kin_fp": "2ebcbf17",
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+  "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,
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+    {
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+    }
+  ],
+  "audit_wall_s": 14.869853199998033
+}
@@ -0,0 +1,71 @@
+{
+  "task_index": 180,
+  "task": {
+    "tid": 737760595,
+    "id": "s0003_001280",
+    "cat": "B",
+    "mode": "pos",
+    "q0": [
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+      -149.808,
+      -8.981,
+      -88.566,
+      83.45
+    ],
+    "T": [
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+      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:
+        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')

@@ -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)
@@ -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


@@ -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()