refactor: 将子模块转为普通目录，移除外部 git 依赖

- 移除 JEPA/lejepa-identifiability 子模块 gitlink
- 移除 research/multiply/MultiPLY 子模块 gitlink
- 删除 .gitmodules（不再有外部 URL 依赖）
- 两个目录内容作为普通文件纳入主仓库追踪
- 删除各自内部 .git 目录，消除嵌套 git 仓库

JEPA/lejepa-identifiability/experiments/lejepa_id/reacher.py

@@ -0,0 +1,95 @@
+"""DMC Reacher rendering and dataset utilities."""
+
+import os
+os.environ.setdefault("MUJOCO_GL", "egl")
+
+import numpy as np
+import torch
+from dm_control import suite
+from tqdm import tqdm
+
+from .data import ou_augment
+
+
+def make_env():
+    return suite.load(domain_name="reacher", task_name="hard")
+
+
+def render_at(env, qpos, target, height=64, width=64):
+    """Set joint angles and render → (3, H, W) float32 in [0, 1]."""
+    env.physics.data.qpos[:2] = qpos
+    env.physics.data.qvel[:] = 0
+    env.physics.named.model.geom_pos['target', :2] = target
+    env.physics.forward()
+    rgb = env.physics.render(height=height, width=width, camera_id=0)
+    return rgb.transpose(2, 0, 1).astype(np.float32) / 255.0
+
+
+def render_batch(env, qpos_batch, target, height=64, width=64):
+    """Render batch → (N, 3, H, W)."""
+    N = len(qpos_batch)
+    imgs = np.empty((N, 3, height, width), dtype=np.float32)
+    for i in tqdm(range(N), desc="Rendering"):
+        imgs[i] = render_at(env, qpos_batch[i], target, height, width)
+    return imgs
+
+
+def generate_ou_image_pairs(env, N, rho, target, seed=9999):
+    """
+    Sample OU latent pairs, render both → (img_t, img_tp1, z_t, z_tp1).
+
+    Uses the same OU process as the rest of the repo but renders through MuJoCo.
+    """
+    rng = np.random.default_rng(seed)
+    z_t = rng.standard_normal((N, 2)).astype(np.float32)
+    eps = rng.standard_normal((N, 2)).astype(np.float32)
+    z_tp1 = rho * z_t + np.sqrt(1 - rho**2) * eps
+
+    print(f"Rendering {2 * N} images (rho={rho})...")
+    img_t = render_batch(env, z_t, target)
+    img_tp1 = render_batch(env, z_tp1, target)
+    return img_t, img_tp1, z_t, z_tp1
+
+
+def normalize_images(img_t, img_tp1, img_eval=None):
+    """Per-channel mean/std normalization. Returns normalized arrays + stats."""
+    mean = img_t.mean(axis=(0, 2, 3), keepdims=True)
+    std = img_t.std(axis=(0, 2, 3), keepdims=True) + 1e-6
+    img_t = (img_t - mean) / std
+    img_tp1 = (img_tp1 - mean) / std
+    if img_eval is not None:
+        img_eval = (img_eval - mean) / std
+        return img_t, img_tp1, img_eval, mean, std
+    return img_t, img_tp1, mean, std
+
+
+def solve_ik_grid(env, target, n_grid=200):
+    """Find joint angles that place fingertip at target via grid search."""
+    best_dist, best_qpos = np.inf, None
+    for q0 in np.linspace(-np.pi, np.pi, n_grid):
+        for q1 in np.linspace(-np.pi, np.pi, n_grid):
+            env.physics.data.qpos[:2] = [q0, q1]
+            env.physics.named.model.geom_pos['target', :2] = target
+            env.physics.forward()
+            tip = env.physics.named.data.geom_xpos['finger'][:2]
+            d = np.linalg.norm(tip - target)
+            if d < best_dist:
+                best_dist = d
+                best_qpos = np.array([q0, q1])
+    return best_qpos, best_dist
+
+
+class ReacherOUDataset(torch.utils.data.Dataset):
+    """Prerendered OU image pairs with ground-truth latents."""
+
+    def __init__(self, img_t, img_tp1, z_t, z_tp1):
+        self.img_t = torch.from_numpy(img_t)
+        self.img_tp1 = torch.from_numpy(img_tp1)
+        self.z_t = torch.from_numpy(z_t)
+        self.z_tp1 = torch.from_numpy(z_tp1)
+
+    def __len__(self):
+        return len(self.img_t)
+
+    def __getitem__(self, i):
+        return self.img_t[i], self.img_tp1[i], self.z_t[i], self.z_tp1[i]