"""闭环仿真（验证 / 挖错 / 门禁共用，PyTorch 批量，无梯度）——按部署节奏运行： 50 Hz 执行；队列 ≤ 3 步时推理一次（已提交前缀 r = 队列长度），提交接下来 3 步； 冷启动用价值头对候选末态打分（短前瞻），之后按与上一条**真正提交**的计划的一致性选候选； 接近目标 < 5 mm 时用局部 DLS 精修；子目标头在新目标时给出途经点，按「越过顶点」规则切下一个（与标签同口径，见 sub_advance）。 safety=False 时不做任何碰撞拦截（暴露真实错误，挖错用）；safety=True 时近似部署安全层（候选提交步有干涉则换下一条，全不过则停一步）。 动作反归一化：inc = clamp(act, −1, 1) · act_scale · STEP；act_scale 来自 checkpoint（旧模型缺省 1.0，见 ml/common/ckpt.load_policy）。 碰撞与到达只在每题自己的时限内计（短题与长题同批时不把超时后的部分算进去）。 python -m ml.sim.closed_loop --ckpt ml/runs/base/best.pt --samples ml/data/samples --split val --n 1000 [--safety]""" import argparse, json, math, os import torch from ml.common.kin_torch import Kin from ml.common.features import features, STEP, VMAX, H, GAP_CAP, D2R, wrap180 DT = 0.02 FIRST_DEG = 5.0 # R7 首次方向判定阈值（度）：小于它的抖动不算选边 SUB_LEAD_S = 0.02 # 子目标「越过顶点」提前量（s）：标签在老师 τ > τ_顶点 − 0.02 时就换到下一个顶点（build_dataset.cjs remain 过滤） SUB_REACH_DEG, SUB_TIMEOUT_S = 5.0, 1.5 # 旧规则（sub_rule='timeout'）：到达 < 5° 或 1.5 s 超时 def tasks_to_tensors(tasks, device): q0 = torch.tensor([t['q0'] for t in tasks], dtype=torch.float32, device=device) T = torch.tensor([t['T'] for t in tasks], dtype=torch.float32, device=device) R = torch.tensor([t['R'] if t.get('R') else [0.] * 9 for t in tasks], dtype=torch.float32, device=device) # 无老师参考（挖错/门禁题）时 exec_s/travel 为 None → NaN；调用方据此区分口径 ex = torch.tensor([t['exec_s'] if t.get('exec_s') else float('nan') for t in tasks], dtype=torch.float32, device=device) tr = torch.tensor([t['travel'] if t.get('travel') else float('nan') for t in tasks], dtype=torch.float32, device=device) return q0, T, R, ex, tr NOREF_SEC = 6.0 # 无老师参考题的定长时限（s） def time_limit(ex, max_sec=None): """每题时限（s）：有老师 = 老师时长 × 2（至少 3 s）；无老师 = NOREF_SEC；max_sec 覆盖全部。""" if max_sec is not None: return torch.full_like(ex, max_sec) return torch.where(torch.isfinite(ex), torch.clamp(2 * ex, min=3.0), torch.full_like(ex, NOREF_SEC)) def goal_err(kin, q, T, R): xf = kin.cum_xf(q) p = kin.fk(q, xf); Rq = kin.orient(q, xf).reshape(-1, 9) e = (T - p).norm(dim=-1) pose = R.abs().sum(-1) > 0 tr = (Rq * R).sum(-1) oe = torch.where(pose, torch.acos(((tr - 1) / 2).clamp(-1, 1)) / D2R, torch.zeros_like(e)) return e, oe def dls_steps(kin, q, T, R, n=3, scale=0.3, lam=0.5): """局部 DLS 精修：n 步增量 [B,n,6]""" step = torch.tensor(STEP, device=q.device) pose = (R.abs().sum(-1) > 0).float() Rt = R.view(-1, 3, 3) def err(x): xf = kin.cum_xf(x); p = kin.fk(x, xf); Rq = kin.orient(x, xf) M = Rt @ Rq.transpose(1, 2) rv = 0.5 * torch.stack([M[:, 2, 1] - M[:, 1, 2], M[:, 0, 2] - M[:, 2, 0], M[:, 1, 0] - M[:, 0, 1]], -1) * 100 * pose[:, None] return torch.cat([T - p, rv], -1) out = []; x = q.clone() for _ in range(n): e0 = err(x); J = [] for j in range(6): dx = torch.zeros_like(x); dx[:, j] = 1e-3 J.append(-(err(x + dx) - e0) / 1e-3) J = torch.stack(J, -1) # [B,6(err),6(q)] A = J @ J.transpose(1, 2) + lam ** 2 * torch.eye(6, device=q.device) y = torch.linalg.solve(A, e0.unsqueeze(-1)) dq = (J.transpose(1, 2) @ y).squeeze(-1) sc = (dq.abs() / (step * scale)).max(-1, keepdim=True).values.clamp(min=1) dq = dq / sc out.append(dq); x = x + dq return torch.stack(out, 1) def jdiff(kin, a, b): """a − b（度），连续轴取最短弧。""" d = a - b return torch.where(kin.cont, wrap180(d), d) def sub_advance(kin, subs, nsub, sub_prev, sub_t, qe, tnow, rule='cross'): """子目标推进（每次规划前调用，每次最多推进一个）。subs [B,M,6] 待走途经点（绝对角），nsub [B] 剩余个数， sub_prev [B,6] 上一个已越过的点（冷启动时 = 起点），sub_t [B] 当前子目标开始时刻。返回 (subs, nsub, sub_prev, sub_t)。 rule='cross'（默认）：qe 沿拐角平分面法向越过当前顶点（提前 SUB_LEAD_S 秒）即切到下一个。 2026-10-19（审查 review_data_actions C5 / review_model_train P0-1）：标签里 sub = 老师「下一个尚未经过的顶点」， 老师 τ 一过顶点（−0.02 s）立刻换；旧规则要等最大关节差 < 5° 或 1.5 s 超时，策略在顶点附近 5–10° 进不去时 sub 一直指着身后的点，这种「sub 已在身后」的特征序列训练里从未出现。运行时没有老师 τ，用几何等价物： 进入方向 a = 顶点 − 上一点、离开方向 b = 下一顶点 − 顶点（最后一个顶点取 a），各轴按 VMAX 折成秒（与 τ 同量纲）， 法向 n = â + b̂；(qe − 顶点)·n̂ ≥ −SUB_LEAD_S 判为越过。没有超时（标签也没有）。 rule='timeout'：旧部署口径，到达 < 5° 或 1.5 s 超时（诊断对照用）。 ik-policy.js Runner（subRule）与 apg_finetune.py 用同一规则，三处同步改。""" has = nsub > 0 if not bool(has.any()): return subs, nsub, sub_prev, sub_t cur = subs[:, 0] if rule == 'timeout': dd = jdiff(kin, cur, qe).abs().max(-1).values adv = has & ((dd < SUB_REACH_DEG) | (tnow - sub_t > SUB_TIMEOUT_S)) elif rule == 'cross': vmax = torch.tensor(VMAX, device=qe.device, dtype=qe.dtype) un = lambda v: v / v.norm(dim=-1, keepdim=True).clamp(min=1e-9) a = jdiff(kin, cur, sub_prev) / vmax nxt = subs[:, 1] if subs.shape[1] > 1 else cur b = torch.where((nsub > 1)[:, None], jdiff(kin, nxt, cur) / vmax, a) n = un(un(a) + un(b)) proj = (jdiff(kin, qe, cur) / vmax * n).sum(-1) adv = has & (proj >= -SUB_LEAD_S) else: raise ValueError('sub_rule: ' + str(rule)) subs = torch.where(adv[:, None, None], torch.cat([subs[:, 1:], subs[:, -1:]], 1), subs) sub_prev = torch.where(adv[:, None], cur, sub_prev) nsub = torch.where(adv, nsub - 1, nsub); sub_t = torch.where(adv, torch.full_like(sub_t, tnow), sub_t) return subs, nsub, sub_prev, sub_t class TeacherOracle: """老师当策略（2026-10-19，审查 review_closedloop D1 / review_data_actions §4.1）： 规划时从当前时间下标 it 取老师 50 Hz 轨迹的 16 步增量，按与 build_dataset.cjs 相同的标签口径 act = clamp(Δ / (act_scale·STEP), −1, 1) 交给 rollout()，其余（队列、前缀、提交、qe、DLS、done、碰撞、时限）一行不改。 traj [B,Tmax,6]：每题老师轨迹，末帧重复补齐（按 id 从 ml/data/shard_*.jsonl 取）。 sigma > 0：每次规划给 act 加 N(0, σ²)（σ 以 STEP 为单位，各候选独立），测闭环对残差噪声的敏感度。 跟随时第 it 步的 q 应等于 traj[:, it]（DLS 接管或到达冻结之后除外）。""" head = 'oracle' def __init__(self, traj, act_scale=1.0, sigma=0.0): self.traj, self.act_scale, self.sigma = traj, float(act_scale), float(sigma) def plan(self, it, K, g): B, Tm, _ = self.traj.shape dev = self.traj.device idx = (it + torch.arange(H + 1, device=dev)).clamp(max=Tm - 1) seg = self.traj[:, idx] astep = torch.tensor(STEP, device=dev) * self.act_scale act = ((seg[:, 1:] - seg[:, :-1]) / astep).clamp(-1, 1) act = act[:, None].expand(B, K, H, 6).contiguous() if self.sigma > 0: act = act + torch.randn(B, K, H, 6, device=dev, generator=g) * (self.sigma / self.act_scale) return act @torch.no_grad() def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, return_traj=False, use_sub=True, sub_override=None, qp0=None, steps=1, z_scale=1.0, select='consist', oracle=None, replan='r3', sub_rule='cross'): """use_sub=False：不用子目标（sf=0）；sub_override=(subs [B,M,6] 绝对关节角, n [B] 个数 或 need [B] bool)：冷启动时用给定途经点代替子目标头； qp0 [B,6]：起点的上一帧（默认 = q0，即静止起步）。steps：MeanFlow 采样段数；z_scale：噪声缩放； select：consist（部署口径：冷启动价值前瞻、之后与上一条已提交计划的一致性）/ value（每次都价值前瞻）/ first（不选，取第 0 条）。 oracle：TeacherOracle，代替 model 出动作（model 可为 None，须配 select='first'、use_sub=False）。 replan：'r3'（部署：队列 ≤ 3 步就规划，稳态 r=3）/ 'empty'（队列空了才规划，每次 r=0，前缀消融用）。 sub_rule：子目标推进规则，见 sub_advance。除默认值外均为诊断用。""" g = torch.Generator(device=device); g.manual_seed(seed) q0, T, R, ex, tr = tasks_to_tensors(tasks, device) B = q0.shape[0] step = torch.tensor(STEP, device=device) act_scale = oracle.act_scale if oracle is not None else float(getattr(model, 'act_scale', 1.0)) astep = step * act_scale # 动作反归一化：每单位 act 对应的度数 has_ref = torch.isfinite(ex) limit = time_limit(ex, max_sec) nmax = int(math.ceil(limit.max().item() / DT)) q, qp = q0.clone(), (qp0.to(device) if qp0 is not None else q0.clone()) queue = torch.zeros(B, 0, 6, device=device) last = None; last_ok = torch.zeros(B, dtype=torch.bool, device=device) subs = torch.zeros(B, 2, 6, device=device); nsub = torch.zeros(B, dtype=torch.long, device=device) sub_t = torch.zeros(B, device=device); sub_prev = q0.clone() done = torch.zeros(B, dtype=torch.bool, device=device); t_done = torch.full((B,), float('nan'), device=device) 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 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) j1_acc = torch.zeros(B, device=device); j1_max = torch.zeros(B, device=device); j1_min = torch.zeros(B, device=device) j1_first = torch.zeros(B, device=device) # 2026-10-18 第五轮（审查 N-3）：J1 累计偏离首次超过 FIRST_DEG 时的方向（真正的"首次方向"） cold = True ar = torch.arange(B, device=device) for it in range(nmax): 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) 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) sf = (nsub > 0).float() feat = features(kin, q, qp, T, R, pre, torch.full((B,), r, device=device), subs[:, 0], sf) if oracle is not None: act = oracle.plan(it, K, g); sub = torch.zeros(B, 13, device=device) else: z = torch.randn(B * K, H, 6, device=device, generator=g) * z_scale act, val, sub = model.sample_k(feat, K, z, steps=steps) inc = act.float().clamp(-1, 1) * astep if cold: need = sub[:, 12] > 0.5 subs = q[:, None] + sub[:, :12].float().view(B, 2, 6) * 180 if sub_override is not None: subs = sub_override[0].to(device).float() n_ov = sub_override[1].to(device) nsub_ov = n_ov.long() if n_ov.dtype != torch.bool else n_ov.long() * subs.shape[1] need = nsub_ov > 0 nsub = nsub_ov else: nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub)) if not use_sub: nsub = torch.zeros_like(nsub) sub_t.fill_(tnow); sub_prev = q.clone() fut = qe[:, None, None] + torch.cumsum(inc[:, :, r:], 2) # [B,K,H-r,6] if select == 'first': score = torch.zeros(B, K, device=device); score[:, 0] = 1 elif cold or select == 'value': # 短前瞻：候选末态的价值 qa = fut[:, :, -1].reshape(B * K, 6); qb = fut[:, :, -2].reshape(B * K, 6) f2 = features(kin, qa, qb, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0), torch.zeros(B * K, 3, 6, device=device), torch.zeros(B * K, device=device), qa, torch.zeros(B * K, device=device)) v2, _ = model.heads(model.encode(f2)) score = -v2.view(B, K) else: # 2026-10-19 修错位：last[i] = 上一计划从 qe_old 走 i 步后的位置；提交 3 步后 qe_new = last[3]， # 新候选 fut[0] 是 qe_new 再走 1 步 = 对应 last[4]。JS 部署（ik-policy.js）同一处同样修正。 n = min(fut.shape[2], last.shape[1] - 4) w = 0.85 ** torch.arange(n, device=device, dtype=torch.float32) d2 = (((fut[:, :, :n] - last[:, None, 4:4 + n]) / step) ** 2).sum(-1) # 2026-10-19（审查 review_closedloop F5）：上一次是刹停或 DLS 接管的行没有「已提交计划」， # 与 JS（lastPlan = null → 一致性项缺席、各候选同分 → 取第 0 条）一致：同分，稳定排序取第 0 条 score = torch.where(last_ok[:, None], -(d2 * w).sum(-1), torch.zeros_like(d2[..., 0])) order = torch.sort(score, dim=-1, descending=True, stable=True).indices pick = order[:, 0] commit = inc[ar, pick, r:r + 3] committed = torch.ones(B, dtype=torch.bool, device=device) if safety: ok_any = torch.zeros(B, dtype=torch.bool, device=device) chosen = torch.zeros(B, 3, 6, device=device); chosen_k = pick.clone() for kk in range(K): ck = inc[ar, order[:, kk], r:r + 3] pts = qe[:, None] + torch.cumsum(ck, 1) mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ck allq = torch.cat([pts, mids], 1).reshape(-1, 6) okk = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 6).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 commit = torch.where(ok_any[:, None, None], chosen, torch.zeros_like(chosen)) brakes += (~ok_any).float() pick = chosen_k; committed = ok_any if near.any(): ref = dls_steps(kin, qe, T, R) commit = torch.where(near[:, None, None], ref, commit) dls_it = torch.where(near & (dls_it < 0), torch.full_like(dls_it, it), dls_it) committed = committed & ~near # 2026-10-19（F5）：last 记真正提交的那条候选（安全层换了候选时是换后的那条）；刹停 / DLS 接管的行无效 last = torch.cat([qe[:, None], fut[ar, pick]], 1); last_ok = committed queue = torch.cat([queue, commit], 1) cold = False d = queue[:, 0]; queue = queue[:, 1:] d = torch.where(done[:, None], torch.zeros_like(d), d) qp = q; q = kin.clamp_box(q + d) travel += d.abs().sum(-1) j1_acc = j1_acc + d[:, 0]; j1_max = torch.maximum(j1_max, j1_acc); j1_min = torch.minimum(j1_min, j1_acc) j1_first = torch.where((j1_first == 0) & (j1_acc.abs() >= FIRST_DEG), torch.sign(j1_acc), j1_first) if it + 1 == n_start: moved_start = travel.clone() if return_traj: traj.append(q.clone()) mid = qp + 0.5 * (q - qp) gm = kin.gaps(torch.cat([q, mid], 0), 0.5)['gm'].min(-1).values tn = (it + 1) * DT # 2026-10-19（审查 review_closedloop F4）：只计每题自己时限内的碰撞（同批长题会把短题多仿真几秒） hit = ((gm[:B] <= 0) | (gm[B:] <= 0)) & ~done & (tn <= limit + 1e-6) col += hit.float() first_hit = torch.where(hit & torch.isnan(first_hit), torch.full_like(first_hit, tn), first_hit) e, oe = goal_err(kin, q, T, R) if it + 1 == k2s: e2 = e.clone() newly = (~done) & (e < 1) & (oe < 0.5) t_done = torch.where(newly, torch.full_like(t_done, tn), t_done); done |= newly if done.all(): break ok = done & (t_done <= limit + 1e-6) if moved_start is None: moved_start = travel.clone() e_end, _ = goal_err(kin, q, T, R) if e2 is None: e2 = e_end ratio2 = e2 / e0.clamp(min=1e-3) start_fail = (moved_start < 1.0) & ~(done & (t_done <= 0.5)) okr = ok & has_ref & torch.isfinite(tr) res = {'n': B, 'n_ref': int(has_ref.sum()), 'success': ok.float().mean().item(), 'time_ok_1p3': (ok & has_ref & (t_done <= 1.3 * ex)).float().sum().item() / max(1, int(has_ref.sum())) if has_ref.any() else None, 'collision_rate': (col > 0).float().mean().item(), 'collision_steps_mean': col.mean().item(), 'travel_ratio_median': (travel[okr] / tr[okr].clamp(min=1)).median().item() if okr.any() else None, 'time_mean_s': t_done[ok].mean().item() if ok.any() else None, 'brakes_mean': brakes.mean().item(), 'start_fail': start_fail.float().mean().item(), 'goal_ratio_2s': ratio2.median().item(), 'time_budget': 'teacher×2(≥3 s)' if bool(has_ref.all()) else ('fixed %.0f s' % (max_sec or NOREF_SEC) if not has_ref.any() else 'mixed')} # 绕行侧：J1 累计位移的正向/负向极值哪个更大（+1 / −1），供 R7 左右比例门禁 # 首次方向；全程没偏离到 FIRST_DEG 的（基本不绕 J1）退回净方向 side = torch.where(j1_first != 0, j1_first, torch.where(j1_max >= -j1_min, torch.ones_like(j1_max), -torch.ones_like(j1_max))) 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() return res, per def evaluate(model, kin, tasks, device, K=8, safety=False, bs=256, seed=0): if not tasks: # 小样本摸底时 val 可能为空 return {'n': 0, 'success': float('nan'), 'collision_rate': float('nan'), 'start_fail': float('nan'), 'brakes_mean': float('nan'), 'goal_ratio_2s': float('nan')}, [] agg, pers = {}, [] for i in range(0, len(tasks), bs): r, p = rollout(model, kin, tasks[i:i + bs], device, K=K, safety=safety, seed=seed + i) w = r['n']; pers.append((r, p)) for k, v in r.items(): if k == 'n' or v is None or isinstance(v, str): continue agg[k] = agg.get(k, 0.0) + v * w n = sum(r['n'] for r, _ in pers) out = {k: v / n for k, v in agg.items()}; out['n'] = n out['n_ref'] = sum(r['n_ref'] for r, _ in pers) # 中位数不能按批加权平均：全体任务重算（2026-10-19，选模分数用它） out['goal_ratio_2s'] = torch.cat([p['goal_ratio_2s'] for _, p in pers]).median().item() out['time_budget'] = pers[0][0]['time_budget'] if len({r['time_budget'] for r, _ in pers}) == 1 else 'mixed' return out, pers def main(): from ml.common.ckpt import load_policy from ml.data import load_tasks ap = argparse.ArgumentParser() ap.add_argument('--ckpt', required=True); ap.add_argument('--samples', default='ml/data/samples') ap.add_argument('--tasks', default=None, help='任务 jsonl（覆盖 --samples/--split）') ap.add_argument('--split', default='val'); ap.add_argument('--n', type=int, default=1000) ap.add_argument('--safety', action='store_true'); ap.add_argument('--K', type=int, default=8) ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--out', default=None) a = ap.parse_args() model, ck = load_policy(a.ckpt, a.device) kin = Kin(device=a.device) assert ck.get('fp') in (None, kin.fp), '碰撞模型指纹与 checkpoint 不符' if a.tasks: tasks = [json.loads(l) for l in open(a.tasks, encoding='utf-8') if l.strip()] else: tasks = [t for t in load_tasks([a.samples]) if t['split'] == a.split] tasks = tasks[:a.n] res, pers = evaluate(model, kin, tasks, a.device, K=a.K, safety=a.safety) by = {} flat_ok = torch.cat([p['ok'] for _, p in pers]); flat_col = torch.cat([p['col'] for _, p in pers]) for t, d, c in zip(tasks, flat_ok.tolist(), flat_col.tolist()): k = t.get('cat', '?') + '/' + t.get('mode', '?'); b = by.setdefault(k, [0, 0, 0]); b[0] += 1; b[1] += int(d); b[2] += int(c > 0) res['by_cat_mode'] = {k: {'n': v[0], 'success': v[1] / v[0], 'collision': v[2] / v[0]} for k, v in by.items()} s = json.dumps(res, indent=1); print(s) if a.out: # 先写 .tmp 再改名：中断不会留下半截结果被当成已完成（M5 续跑按文件存在跳过） open(a.out + '.tmp', 'w').write(s); os.replace(a.out + '.tmp', a.out) if __name__ == '__main__': main()