diff --git a/ml/common/ckpt.py b/ml/common/ckpt.py index f3a714a..de82e2c 100644 --- a/ml/common/ckpt.py +++ b/ml/common/ckpt.py @@ -72,6 +72,7 @@ def load_policy(path, device, prefer_ema=True): sd = ck.get('ema') if prefer_ema and ck.get('ema') is not None else ck['model'] m.load_state_dict(sd) m.act_scale = float(ck.get('act_scale', 1.0)) + m.abs_target = bool(ck.get('abs_target', False)) return m.eval(), ck diff --git a/ml/data.py b/ml/data.py index b678e06..5d80afb 100644 --- a/ml/data.py +++ b/ml/data.py @@ -43,6 +43,9 @@ class Samples: sc = {float(m['act_scale']) for m in metas} if len(sc) > 1: raise SystemExit(f'样本目录的 act_scale 不一致 {sorted(sc)}（{dirs}）：DAgger 样本须与主数据同一版打包') self.act_scale = sc.pop(); self.data_ver = int(metas[0].get('data_ver', 1)) if metas else 1 + ab = {bool(m.get('abs_target', False)) for m in metas} + if len(ab) > 1: raise SystemExit(f'样本目录的 abs_target 不一致 {sorted(ab)}（{dirs}）：绝对目标与增量标签不能混用') + self.abs_target = ab.pop() if ab else False for d in dirs: for fx in sorted(glob.glob(os.path.join(d, '*.X.npy'))): b = fx[:-6] @@ -87,8 +90,14 @@ class Samples: km = (torch.arange(3, device=dev)[None] < pl[:, None]) & m[:, None] # [B,3] 前缀有效步 eps = rn(B, 3, 6) * ps * km[..., None].to(X.dtype) # STEP 为单位 X[:, PREFIX_SL] = X[:, PREFIX_SL] + eps.reshape(B, 18) - Y[:, :3] = Y[:, :3] + eps / self.act_scale # Y 以 act_scale·STEP 为单位 - Y[torch.arange(B, device=dev), pl.clamp(max=Y.shape[1] - 1)] -= eps.sum(1) / self.act_scale + if self.abs_target: + # 2026-10-19（方案 B）：标签是绝对目标角（/180）。前缀噪声会真实执行 → 前 r 步绝对目标 + # 跟着累计偏移（eps 加进前缀特征时就是度），第 r 步扣回、之后回到原轨迹。 + Y[:, :3] = Y[:, :3] + torch.cumsum(eps, 1) / 180.0 + Y[torch.arange(B, device=dev), pl.clamp(max=Y.shape[1] - 1)] -= eps.sum(1) / 180.0 + else: + Y[:, :3] = Y[:, :3] + eps / self.act_scale # Y 以 act_scale·STEP 为单位 + Y[torch.arange(B, device=dev), pl.clamp(max=Y.shape[1] - 1)] -= eps.sum(1) / self.act_scale Y.clamp_(-1, 1) ds = float(cfg.get('aug_dq_sigma', 0.0)) if ds > 0: diff --git a/ml/prep/build_dataset.cjs b/ml/prep/build_dataset.cjs index 1930d0b..22961a3 100644 --- a/ml/prep/build_dataset.cjs +++ b/ml/prep/build_dataset.cjs @@ -25,7 +25,10 @@ const DATA_VER = 2; const MIN_GAP_MM = +(process.env.MIN_GAP_MM || 3); const AUG_P = +(process.env.AUG_P || 0.4), AUG_SIGMA = +(process.env.AUG_SIGMA || 3), AUG_BACK = +(process.env.AUG_BACK || 12); const ACT_SCALE = +(process.env.ACT_SCALE || 1.25); -const SIG = ['v' + DATA_VER, 's' + ACT_SCALE, 'p' + AUG_P, 'sg' + AUG_SIGMA, 'b' + AUG_BACK, 'g' + MIN_GAP_MM].join('|'); +/* 2026-10-19（方案 B）：ABS_TARGET=1 时标签改为 16 步绝对目标关节角（/180 归一化），执行端每步朝目标走； + prefix 仍存增量（度），与特征/部署的历史动作语义一致。未设时行为与原来完全一致。 */ +const ABS_TARGET = +(process.env.ABS_TARGET || 0); +const SIG = ['v' + DATA_VER, 's' + ACT_SCALE, 'a' + ABS_TARGET, 'p' + AUG_P, 'sg' + AUG_SIGMA, 'b' + AUG_BACK, 'g' + MIN_GAP_MM].join('|'); if (process.argv[2] === '--sig') { console.log(SIG); process.exit(0); } const IN = process.argv[2], OUTDIR = process.argv[3] || path.join(__dirname, '../data/samples'); if (!IN) { console.error('usage: build_dataset.cjs [outDir] | --sig'); process.exit(1); } @@ -74,14 +77,30 @@ for (let li = 0; li < NT; li++) { } const pos = k => { const i = Math.min(N, t + k); const w = aug ? Math.max(0, 1 - k / AUG_BACK) : 0; return traj[i].map((v, j) => v + off[j] * w); }; const y = new Float32Array(H * 6); - /* prevP = 按标签实际会走到的位置：被 ±1 截掉的亏空留在 prevP 与目标之差里，下一步接着补（2026-10-19，C2） */ - let prevP = q.slice(); - for (let k = 1; k <= H; k++) { - const pk = pos(k); - for (let j = 0; j < 6; j++) { const v = Math.max(-1, Math.min(1, (pk[j] - prevP[j]) / ASTEP[j])); y[(k - 1) * 6 + j] = v; prevP[j] += v * ASTEP[j]; } + if (ABS_TARGET) { + for (let k = 1; k <= H; k++) { + const pk = pos(k); + for (let j = 0; j < 6; j++) y[(k - 1) * 6 + j] = Math.max(-1, Math.min(1, pk[j] / 180)); + } + } else { + /* prevP = 按标签实际会走到的位置：被 ±1 截掉的亏空留在 prevP 与目标之差里，下一步接着补（2026-10-19，C2） */ + let prevP = q.slice(); + for (let k = 1; k <= H; k++) { + const pk = pos(k); + for (let j = 0; j < 6; j++) { const v = Math.max(-1, Math.min(1, (pk[j] - prevP[j]) / ASTEP[j])); y[(k - 1) * 6 + j] = v; prevP[j] += v * ASTEP[j]; } + } } const rr = Math.floor(rnd() * 4), prefix = []; - for (let k = 0; k < rr; k++) { const row = []; for (let j = 0; j < 6; j++) row.push(y[k * 6 + j] * ASTEP[j]); prefix.push(row); } + for (let k = 0; k < rr; k++) { + const row = []; + for (let j = 0; j < 6; j++) { + if (ABS_TARGET) { // 前缀仍存「已提交的增量（度）」，特征语义不变 + const cur = y[k * 6 + j] * 180, prev = k ? y[(k - 1) * 6 + j] * 180 : q[j]; + row.push(cur - prev); + } else row.push(y[k * 6 + j] * ASTEP[j]); + } + prefix.push(row); + } const remain = inter.filter(v => v.tau > taus[t] + 0.02); const sub = remain.length && rnd() < 0.8 ? remain[0].p : null; const gm = S.gapMatrix(q, POL.GAP_CAP); @@ -102,7 +121,7 @@ writeNpy(path.join(OUTDIR, base + '.V.npy'), Float32Array.from(V), [n]); writeNpy(path.join(OUTDIR, base + '.SG.npy'), cat(SG, 13), [n, 13]); writeNpy(path.join(OUTDIR, base + '.TID.npy'), TID, [n], 'int32'); /* meta 在 tasks.jsonl 之前写：tasks.jsonl 仍是「本 shard 打包完成」的标记 */ -fs.writeFileSync(path.join(OUTDIR, base + '.meta.json'), JSON.stringify({ data_ver: DATA_VER, act_scale: ACT_SCALE, aug_p: AUG_P, aug_sigma: AUG_SIGMA, +fs.writeFileSync(path.join(OUTDIR, base + '.meta.json'), JSON.stringify({ data_ver: DATA_VER, act_scale: ACT_SCALE, abs_target: ABS_TARGET, aug_p: AUG_P, aug_sigma: AUG_SIGMA, aug_back: AUG_BACK, min_gap_mm: MIN_GAP_MM, aug_carry: true, sig: SIG })); // 2026-10-18 断点续跑：tasks.jsonl 最后写、先写 .tmp 再改名 —— 它存在就代表这个 shard 的 5 个 npy 都已写完，run_prep.sh 据此跳过 const tf = path.join(OUTDIR, base + '.tasks.jsonl'); diff --git a/ml/sim/closed_loop.py b/ml/sim/closed_loop.py index 4823a82..7232c2b 100644 --- a/ml/sim/closed_loop.py +++ b/ml/sim/closed_loop.py @@ -153,7 +153,10 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, 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 + # 方案 B（2026-10-19）：abs_target 模型输出 16 步绝对目标角（/180 归一化），执行时直接朝目标走； + # oracle / 旧 ckpt 仍是增量口径 + abs_t = oracle is None and bool(getattr(model, 'abs_target', False)) + astep = torch.full((6,), 180.0, device=device) if abs_t else step * act_scale has_ref = torch.isfinite(ex) limit = time_limit(ex, max_sec) nmax = int(math.ceil(limit.max().item() / DT)) @@ -177,8 +180,13 @@ 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) + if abs_t and r > 0: # queue 存绝对目标：前缀特征仍用增量（度） + prevq = torch.cat([q[:, None], queue[:, :r - 1]], 1) + pre[:, :r] = queue[:, :r] - prevq + else: + pre[:, :r] = queue[:, :r] + qe = (queue[:, -1] if r > 0 else q) if abs_t else 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) @@ -189,7 +197,7 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, 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 + inc = act.float().clamp(-1, 1) * astep # 增量口径=步长增量；B 口径=绝对目标角 if cold: need = sub[:, 12] > 0.5 subs = q[:, None] + sub[:, :12].float().view(B, 2, 6) * 180 @@ -203,7 +211,7 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, 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] + fut = inc[:, :, r:] if abs_t else qe[:, None, None] + torch.cumsum(inc[:, :, r:], 2) # [B,K,H-r,6] 绝对位置 if select == 'first': score = torch.zeros(B, K, device=device); score[:, 0] = 1 elif cold or select == 'value': @@ -231,8 +239,12 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, 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 + if abs_t: + pts = ck + mids = 0.5 * (torch.cat([qe[:, None], ck[:, :-1]], 1) + ck) + else: + 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 @@ -242,6 +254,7 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, pick = chosen_k; committed = ok_any if near.any(): ref = dls_steps(kin, qe, T, R) + if abs_t: ref = qe[:, None] + torch.cumsum(ref, 1) # 增量 → 绝对目标 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 @@ -250,9 +263,12 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, 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) + if abs_t: # 绝对目标：直接走到目标；到位的题原地冻结 + qp = q; q = kin.clamp_box(torch.where(done[:, None], q, d)); travel += (q - qp).abs().sum(-1) + else: + 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() diff --git a/ml/train.py b/ml/train.py index ad6ddf6..40679d8 100644 --- a/ml/train.py +++ b/ml/train.py @@ -58,8 +58,12 @@ def open_loop(model, kin, vdata, stepv, fk_idx, n=1024, K=8): act, _, _ = model.sample_k(X, K) act = act.float().clamp(-1, 1) q = q_from_feat(X) - qt = q + (Y * stepv).sum(1) - qh = q[:, None] + (act * stepv).sum(2) # [B,K,6] + if bool(getattr(model, 'abs_target', False)): + qt = Y[:, -1] * 180.0 + qh = act[:, :, -1] * 180.0 # [B,K,6] + else: + qt = q + (Y * stepv).sum(1) + qh = q[:, None] + (act * stepv).sum(2) # [B,K,6] pt = fk_points(kin, qt, fk_idx)[:, None] ph = fk_points(kin, qh.reshape(-1, 6), fk_idx).view(X.shape[0], K, -1, 3) err = (ph - pt).norm(dim=-1).mean(-1) # [B,K] mm @@ -98,13 +102,13 @@ def main(): kin = Kin(device=dev) # 2026-10-19（审查 F11）：val 只取主数据目录（第一个 --samples）；DAgger 目录的样本全部当 train data = Samples(a.samples, device=dev, split='train', val_dirs=a.samples[:1]) - act_scale = data.act_scale + act_scale = data.act_scale; abs_t = bool(data.abs_target) val_tasks = [t for t in load_tasks(a.samples[:1]) if t['split'] == 'val'][:tc['eval_tasks']] try: vdata = Samples(a.samples[:1], device=dev, split='val') except SystemExit: vdata = None if vdata is not None and vdata.n == 0: vdata = None - print(f'train samples {data.n}, val tasks {len(val_tasks)}, act_scale {act_scale}, data_ver {data.data_ver}, device {dev}', flush=True) - model = build(cfg).to(dev); model.act_scale = act_scale + print(f'train samples {data.n}, val tasks {len(val_tasks)}, act_scale {act_scale}, abs_target {abs_t}, data_ver {data.data_ver}, device {dev}', flush=True) + model = build(cfg).to(dev); model.act_scale = act_scale; model.abs_target = abs_t print(f'params {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M', flush=True) opt = torch.optim.AdamW(model.parameters(), lr=tc['lr'], betas=tuple(tc['betas']), weight_decay=tc['wd']) step0, best, bad_evals = 0, -1.0, 0 @@ -112,11 +116,14 @@ def main(): s0 = float(ck.get('act_scale', 1.0)) if abs(s0 - act_scale) > 1e-9: raise SystemExit(f'{src} 的 act_scale={s0} 与样本 act_scale={act_scale} 不一致：标签尺度变了，须从头训练（FRESH_FROM=prep）') + a0 = bool(ck.get('abs_target', False)) + if a0 != abs_t: + raise SystemExit(f'{src} 的 abs_target={a0} 与样本 {abs_t} 不一致：标签口径变了，须从头训练（FRESH_FROM=prep）') if init_ck is not None and not a.resume: check_scale(init_ck, a.init) model.load_state_dict(init_ck.get('ema') if init_ck.get('ema') is not None else init_ck['model']) print(f'微调：权重取自 {a.init}（{"EMA" if init_ck.get("ema") is not None else "原始"}权重），优化器重置', flush=True) - ema = copy.deepcopy(model).eval(); ema.act_scale = act_scale + ema = copy.deepcopy(model).eval(); ema.act_scale = act_scale; ema.abs_target = abs_t for p_ in ema.parameters(): p_.requires_grad_(False) if a.resume: ck = torch.load(a.resume, map_location=dev); check_scale(ck, a.resume) @@ -131,7 +138,8 @@ def main(): lr_at = lambda s: tc['lr'] * (s / tc['warmup'] if s < tc['warmup'] else 0.5 * (1 + math.cos(math.pi * (s - tc['warmup']) / max(1, total - tc['warmup'])))) amp = dev.startswith('cuda') and tc.get('amp') == 'bf16' stepv = torch.tensor(STEP, device=dev) - astep = stepv * act_scale # 动作 → 度 + # 方案 B：绝对目标标签（/180 归一化）→ 每单位动作 = 180°；增量口径 = act_scale·STEP + astep = torch.full((6,), 180.0, device=dev) if abs_t else stepv * act_scale fk_idx = torch.linspace(0, kin.cap_a.shape[0] - 1, tc['fk_caps'], device=dev).long() ema_d = float(tc.get('ema', 0.999)) warm = 0 if model.head == 'wta' else int(tc.get('p_neq_warm', 5000)) @@ -144,7 +152,7 @@ def main(): saver = Saver(tc.get('ckpt_min', 10)) # 2026-10-18：每 10 分钟 + 收到停止请求时存 last.pt（原子写） def save(name, step, extra=None): d = {'model': model.state_dict(), 'ema': ema.state_dict(), 'opt': opt.state_dict(), 'cfg': cfg, 'step': step, 'best': best, - 'bad_evals': bad_evals, 'fp': kin.fp, 'act_scale': act_scale, 'data_ver': data.data_ver} + 'bad_evals': bad_evals, 'fp': kin.fp, 'act_scale': act_scale, 'abs_target': abs_t, 'data_ver': data.data_ver} if name == 'last.pt': d['rng'] = rng_state() if extra: d.update(extra) atomic_save(d, os.path.join(a.out, name)) @@ -172,12 +180,17 @@ def main(): l_sm = torch.zeros((), device=dev); l_fk = torch.zeros((), device=dev) if xh.shape[0] > 0: d2 = xh[:, 2:] - 2 * xh[:, 1:-1] + xh[:, :-2] + if abs_t: d2 = d2 * 180.0 / (sum(STEP) / len(STEP)) # 绝对目标 → 与增量口径同量纲（度/STEP） l_sm = d2.pow(2).mean() if tc['w_fk'] > 0 and xh.shape[0] > 0: q = q_from_feat(Xf) ks = torch.tensor([3, 7, 11, 15], device=dev) - qh = q[:, None] + torch.cumsum(xh.clamp(-1, 1) * astep, 1)[:, ks] - qt = q[:, None] + torch.cumsum(Yf * astep, 1)[:, ks] + if abs_t: + qh = (xh.clamp(-1, 1) * 180.0)[:, ks] + qt = (Yf * 180.0)[:, ks] + else: + qh = q[:, None] + torch.cumsum(xh.clamp(-1, 1) * astep, 1)[:, ks] + qt = q[:, None] + torch.cumsum(Yf * astep, 1)[:, ks] B4 = qh.shape[0] * 4 ph = fk_points(kin, qh.reshape(B4, 6), fk_idx); pt = fk_points(kin, qt.reshape(B4, 6), fk_idx) l_fk = ((ph - pt).norm(dim=-1) / 10).mean()