"""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（到达 < 5° 或 1.5 s 超时后切下一个）。 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）。 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, 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 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 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) @torch.no_grad() def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, return_traj=False): 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) has_ref = torch.isfinite(ex) limit = torch.where(has_ref, torch.clamp(2 * ex, min=3.0), torch.full_like(ex, NOREF_SEC)) if max_sec is None else torch.full_like(ex, max_sec) nmax = int(math.ceil(limit.max().item() / DT)) q, qp = q0.clone(), q0.clone() queue = torch.zeros(B, 0, 6, device=device) last = None subs = torch.zeros(B, 2, 6, device=device); nsub = torch.zeros(B, dtype=torch.long, device=device); sub_t = torch.zeros(B, device=device) 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) 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° 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 for it in range(nmax): tnow = it * DT if queue.shape[1] <= 3: 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)) # 子目标推进 if (nsub > 0).any(): cur = subs[:, 0] dd = torch.where(kin.cont, wrap180(cur - qe), cur - qe).abs().max(-1).values adv = (nsub > 0) & ((dd < 5) | (tnow - sub_t > 1.5)) subs = torch.where(adv[:, None, None], torch.cat([subs[:, 1:], subs[:, 1:]], 1), subs) nsub = torch.where(adv, nsub - 1, nsub); sub_t = torch.where(adv, torch.full_like(sub_t, tnow), sub_t) sf = (nsub > 0).float() feat = features(kin, q, qp, T, R, pre, torch.full((B,), r, device=device), subs[:, 0], sf) z = torch.randn(B * K, H, 6, device=device, generator=g) act, val, sub = model.sample_k(feat, K, z) inc = act.clamp(-1, 1) * step if cold: sub0 = sub need = sub0[:, 12] > 0.5 subs = q[:, None] + sub0[:, :12].view(B, 2, 6) * 180 nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub)); sub_t.fill_(tnow) fut = qe[:, None, None] + torch.cumsum(inc[:, :, r:], 2) # [B,K,H-r,6] if last is None: # 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: n = min(fut.shape[2], last.shape[1] - 3) w = 0.85 ** torch.arange(n, device=device, dtype=torch.float32) d2 = (((fut[:, :, :n] - last[:, None, 3:3 + n]) / step) ** 2).sum(-1) score = -(d2 * w).sum(-1) order = score.argsort(-1, descending=True) pick = order[:, 0] commit = inc[torch.arange(B), pick, r:r + 3] if safety: ok_any = torch.zeros(B, dtype=torch.bool, device=device) chosen = torch.zeros(B, 3, 6, device=device) for kk in range(K): ck = inc[torch.arange(B), 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); ok_any |= okk commit = torch.where(ok_any[:, None, None], chosen, torch.zeros_like(chosen)) brakes += (~ok_any).float() if near.any(): ref = dls_steps(kin, qe, T, R) commit = torch.where(near[:, None, None], ref, commit) last = torch.cat([qe[:, None], fut[torch.arange(B), pick]], 1) 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 hit = ((gm[:B] <= 0) | (gm[B:] <= 0)) & ~done col += hit.float() e, oe = goal_err(kin, q, T, R) newly = (~done) & (e < 1) & (oe < 0.5) t_done = torch.where(newly, torch.full_like(t_done, (it + 1) * DT), t_done); done |= newly if done.all(): break ok = done & (t_done <= limit) if moved_start is None: moved_start = travel.clone() e_end, _ = goal_err(kin, q, T, R) 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(), '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(), 't_done': t_done.cpu(), 'col': col.cpu(), 'travel': travel.cpu(), 'err': e_end.cpu(), 'side': side.cpu(), 'start_fail': start_fail.cpu(), 'brakes': brakes.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')}, [] 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) 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.model.policy import build 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() ck = torch.load(a.ckpt, map_location=a.device) model = build(ck['cfg']).to(a.device); model.load_state_dict(ck['model']); model.eval() 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_done = torch.cat([p['done'] for _, p in pers]); flat_col = torch.cat([p['col'] for _, p in pers]) for t, d, c in zip(tasks, flat_done.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()