Hark
2a0a2e55ad
- M1 数据生成器：采样/老师/距离下界认证/时间律/统计/一致性门禁；确定性老师（碰撞次数计时 + resetSession）、pos 任务冻结 J6、--consensus 邻域共识过滤、Dr 类。 - 全流程代码与运行手册：数据打包/可微运动学与对拍/MeanFlow+WTA 模型/闭环仿真/APG/挖错循环/ONNX 导出；webapp 策略运行时 ik-policy.js、?policy=1 接入、tests/policy_bench.cjs；ik-solver 增 pairList/clusterExport/criticalPairs/pairGap/leverTable。
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
"""无老师出题（PyTorch）：A 均匀 / B 直线被挡 / C 贴立柱 / D 工作空间外；pos/pose 各半。"""
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import math
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import torch
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from ml.common.features import wrap180
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def rand_q(kin, n, g):
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u = torch.rand(n, 6, device=kin.lo.device, generator=g)
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return kin.lo + u * (kin.hi - kin.lo)
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def free_q(kin, n, g, chunk=4096):
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out = []
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while sum(x.shape[0] for x in out) < n:
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q = rand_q(kin, chunk, g)
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ok = kin.gaps(q, 0.5)['gm'].min(-1).values > 0
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out.append(q[ok])
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return torch.cat(out)[:n]
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def straight_blocked(kin, a, b, n=24):
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d = torch.where(kin.cont, wrap180(b - a), b - a)
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s = torch.linspace(0, 1, n, device=a.device)
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pts = (a[:, None] + s[None, :, None] * d[:, None]).reshape(-1, 6)
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return (kin.gaps(pts, 0.5)['gm'].min(-1).values <= 0).view(a.shape[0], n).any(-1)
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def column_gap(kin, q):
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gm = kin.gaps(q, 40)['gm']
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return torch.stack([gm[:, g] for g in (2, 3, 4, 5, 6)], -1).min(-1).values # 组 0（立柱/底座）× 组 2..6
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@torch.no_grad()
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def sample_tasks(kin, n, seed, mix=(0.3, 0.3, 0.3, 0.1)):
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g = torch.Generator(device=kin.lo.device); g.manual_seed(seed)
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want = [int(round(n * m)) for m in mix]; want[0] += n - sum(want)
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tasks = []
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def emit(cat, q0, b, T=None):
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pose = torch.rand(q0.shape[0], device=q0.device, generator=g) < 0.5
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if cat == 'D': pose[:] = False
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xf = kin.cum_xf(b)
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Tb = kin.fk(b, xf) if T is None else T
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Rb = kin.orient(b, xf).reshape(-1, 9)
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for i in range(q0.shape[0]):
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tasks.append({'cat': cat, 'mode': 'pose' if pose[i] else 'pos', 'q0': q0[i].tolist(), 'T': Tb[i].tolist(),
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'R': Rb[i].tolist() if pose[i] else None, 'exec_s': 3.0, 'travel': 0.0})
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if want[0]:
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emit('A', free_q(kin, want[0], g), free_q(kin, want[0], g))
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for cat, k in (('B', want[1]), ('C', want[2])):
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got_a, got_b, have = [], [], 0
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while have < k:
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a, b = free_q(kin, 2048, g), free_q(kin, 2048, g)
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m = straight_blocked(kin, a, b)
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if cat == 'C': m &= column_gap(kin, b) < 8
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got_a.append(a[m]); got_b.append(b[m]); have += int(m.sum())
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if k: emit(cat, torch.cat(got_a)[:k], torch.cat(got_b)[:k])
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if want[3]:
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a, b = free_q(kin, want[3], g), rand_q(kin, want[3], g)
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o = kin.fk(torch.zeros(1, 6, device=a.device))
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T = o + (kin.fk(b) - o) * 1.6
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emit('D', a, b, T)
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return tasks
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def neighbours(t, k, g):
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"""±5 mm / ±3° 邻居"""
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out = []
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for _ in range(k):
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u = torch.randn(3, generator=g); u = u / u.norm() * 5.0
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nt = dict(t); nt['T'] = [x + float(d) for x, d in zip(t['T'], u)]
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if t.get('R'):
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ax = torch.randn(3, generator=g); ax = ax / ax.norm(); ang = math.radians(3.0) * float(torch.rand(1, generator=g))
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K = torch.tensor([[0, -ax[2], ax[1]], [ax[2], 0, -ax[0]], [-ax[1], ax[0], 0]])
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Rr = torch.eye(3) + math.sin(ang) * K + (1 - math.cos(ang)) * K @ K
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nt['R'] = (Rr @ torch.tensor(t['R']).view(3, 3)).flatten().tolist()
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nt['hard'] = True
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out.append(nt)
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return out
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