diff --git a/check_all_episodes.py b/check_all_episodes.py
new file mode 100644
index 0000000..2734216
--- /dev/null
+++ b/check_all_episodes.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+检查所有 episode 的重复帧情况
+
+找出哪些 episode 有问题，需要删除或重新收集
+"""
+import os
+import h5py
+import glob
+import numpy as np
+
+
+def check_all_episodes():
+    """检查所有 episode 的质量"""
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+    episode_files = sorted(episode_files, key=lambda x: int(x.split('_')[-1].replace('.hdf5', '')))
+
+    print("="*80)
+    print("所有 Episode 质量检查")
+    print("="*80)
+
+    good_episodes = []
+    bad_episodes = []
+
+    for ep_idx, ep_file in enumerate(episode_files):
+        ep_name = os.path.basename(ep_file).replace('.hdf5', '')
+
+        try:
+            with h5py.File(ep_file, 'r') as f:
+                img_path = '/observations/images/top'
+                if img_path not in f:
+                    continue
+
+                images = f[img_path][:]
+
+                # 检查前 50 帧的重复情况
+                check_frames = min(50, len(images))
+                duplicate_count = 0
+
+                for i in range(check_frames - 1):
+                    img1 = images[i]
+                    img2 = images[i + 1]
+                    diff = np.mean(np.abs(img1.astype(float) - img2.astype(float)))
+
+                    if diff < 1.0:  # 重复
+                        duplicate_count += 1
+
+                duplicate_rate = duplicate_count / check_frames * 100
+
+                # 判断质量
+                if duplicate_rate > 10:  # 超过10%重复
+                    bad_episodes.append((ep_idx, ep_name, duplicate_rate, duplicate_count))
+                    status = "❌"
+                else:
+                    good_episodes.append((ep_idx, ep_name, duplicate_rate, duplicate_count))
+                    status = "✅"
+
+                print(f"{status} Episode {ep_idx:2d}: {duplicate_rate:5.1f}% 重复 ({duplicate_count:2d}/{check_frames}) - {ep_name}")
+
+        except Exception as e:
+            print(f"❌ Episode {ep_idx}: 错误 - {e}")
+
+    # 总结
+    print("\n" + "="*80)
+    print("总结")
+    print("="*80)
+    print(f"总共检查: {len(episode_files)} 个 episodes")
+    print(f"正常的: {len(good_episodes)} 个 ✅")
+    print(f"有问题的: {len(bad_episodes)} 个 ❌")
+
+    if bad_episodes:
+        print(f"\n有问题的 episodes:")
+        for ep_idx, ep_name, rate, count in bad_episodes:
+            print(f"  - episode_{ep_idx}.hdf5: {rate:.1f}% 重复")
+
+        print(f"\n删除命令:")
+        ep_names = [name for _, name, _, _ in bad_episodes]
+        print(f"  rm " + " ".join([f"{dataset_dir}/{name}.hdf5" for name in ep_names]))
+
+    print(f"\n建议:")
+    if len(bad_episodes) > 0:
+        print(f"  1. 删除有问题的 {len(bad_episodes)} 个 episodes")
+        print(f"  2. 重新收集数据，或使用剩余的 {len(good_episodes)} 个正常 episodes")
+    else:
+        print(f"  ✅ 所有 episodes 都正常，可以直接使用！")
+
+
+if __name__ == "__main__":
+    check_all_episodes()
diff --git a/check_specific_frames.py b/check_specific_frames.py
new file mode 100644
index 0000000..ce93d35
--- /dev/null
+++ b/check_specific_frames.py
@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""
+检查特定帧的图像 - 用于验证数据记录问题
+
+功能：
+1. 提取每个 episode 的第 0、1、2 帧图像
+2. 对比不同 episode 的相同帧号
+3. 保存图像供人工检查
+"""
+import os
+import h5py
+import glob
+import cv2
+import numpy as np
+
+
+def check_specific_frames(frame_indices=[0, 1, 2], camera='top', num_episodes=10):
+    """
+    检查特定帧的图像和 qpos
+
+    Args:
+        frame_indices: 要检查的帧索引列表
+        camera: 相机名称
+        num_episodes: 要检查的 episode 数量
+    """
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+    # 按数字排序
+    episode_files = sorted(episode_files, key=lambda x: int(x.split('_')[-1].replace('.hdf5', '')))
+
+    # 创建输出目录
+    output_dir = f'/tmp/dataset_frames'
+    os.makedirs(output_dir, exist_ok=True)
+
+    print(f"检查前 {min(num_episodes, len(episode_files))} 个 episode 的特定帧")
+    print(f"帧索引: {frame_indices}")
+    print(f"相机: {camera}")
+    print("="*80)
+
+    # 收集所有数据
+    for ep_idx in range(min(num_episodes, len(episode_files))):
+        ep_file = episode_files[ep_idx]
+        ep_name = os.path.basename(ep_file).replace('.hdf5', '')
+
+        try:
+            with h5py.File(ep_file, 'r') as f:
+                # 读取 qpos
+                qpos = f['/observations/qpos'][:]
+
+                # 读取图像
+                img_path = f'/observations/images/{camera}'
+                if img_path not in f:
+                    print(f"Episode {ep_name}: 相机 {camera} 不存在")
+                    continue
+
+                images = f[img_path][:]
+
+                print(f"\nEpisode {ep_name}:")
+                print(f"  总帧数: {len(images)}")
+
+                # 保存指定帧
+                for frame_idx in frame_indices:
+                    if frame_idx >= len(images):
+                        print(f"  帧 {frame_idx}: 超出范围")
+                        continue
+
+                    # 保存图像
+                    img = images[frame_idx]
+                    filename = f"{output_dir}/ep{ep_idx:02d}_frame{frame_idx:03d}.png"
+                    cv2.imwrite(filename, img)
+
+                    # 打印 qpos
+                    q = qpos[frame_idx]
+                    print(f"  帧 {frame_idx}: qpos[0:3]=[{q[0]:6.2f}, {q[1]:6.2f}, {q[2]:6.2f}], qpos[3]={q[3]:6.2f} → {filename}")
+
+        except Exception as e:
+            print(f"Episode {ep_name}: 错误 - {e}")
+
+    print("\n" + "="*80)
+    print(f"✅ 所有图像已保存到: {output_dir}")
+    print(f"\n查看方法:")
+    print(f"  eog {output_dir}/*.png")
+    print(f"  ")
+    print(f"  # 或对比特定帧:")
+    print(f"  eog {output_dir}/*_frame000.png  # 所有 episode 的第 0 帧")
+    print(f"  eog {output_dir}/*_frame001.png  # 所有 episode 的第 1 帧")
+    print(f"  eog {output_dir}/*_frame002.png  # 所有 episode 的第 2 帧")
+
+
+def compare_frame_across_episodes(frame_idx=0, camera='top', num_episodes=10):
+    """
+    并排对比所有 episode 的某一帧
+
+    生成一个大的对比图，包含所有 episode 的指定帧
+    """
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+    episode_files = sorted(episode_files, key=lambda x: int(x.split('_')[-1].replace('.hdf5', '')))
+
+    num_compare = min(num_episodes, len(episode_files))
+    cols = 5  # 每行 5 个
+    rows = (num_compare + cols - 1) // cols
+
+    # 创建输出目录
+    output_dir = f'/tmp/dataset_frames'
+    os.makedirs(output_dir, exist_ok=True)
+
+    print(f"生成对比图: 所有 Episode 的第 {frame_idx} 帧")
+    print("="*80)
+
+    # 收集图像
+    images_compare = []
+    qpos_list = []
+
+    for ep_idx in range(num_compare):
+        ep_file = episode_files[ep_idx]
+        ep_name = os.path.basename(ep_file).replace('.hdf5', '')
+
+        try:
+            with h5py.File(ep_file, 'r') as f:
+                qpos = f['/observations/qpos'][:]
+                img_path = f'/observations/images/{camera}'
+
+                if img_path in f and frame_idx < f[img_path].shape[0]:
+                    img = f[img_path][frame_idx]
+                    images_compare.append(img)
+                    qpos_list.append(qpos[frame_idx])
+                    print(f"Episode {ep_name}: qpos[0:3]=[{qpos[frame_idx][0]:.2f}, {qpos[frame_idx][1]:.2f}, {qpos[frame_idx][2]:.2f}]")
+
+        except Exception as e:
+            print(f"Episode {ep_name}: 错误 - {e}")
+
+    if not images_compare:
+        print("❌ 没有收集到图像")
+        return
+
+    # 获取图像尺寸
+    h, w = images_compare[0].shape[:2]
+
+    # 创建对比图
+    compare_img = np.zeros((rows * h + 50, cols * w, 3), dtype=np.uint8)
+
+    for i, (img, qpos) in enumerate(zip(images_compare, qpos_list)):
+        row = i // cols
+        col = i % cols
+
+        y_start = row * h + 30
+        y_end = y_start + h
+        x_start = col * w
+        x_end = x_start + w
+
+        # 调整大小（如果需要）
+        if img.shape[:2] != (h, w):
+            img = cv2.resize(img, (w, h))
+
+        compare_img[y_start:y_end, x_start:x_end] = img
+
+        # 添加信息
+        ep_name = f"Ep {i}"
+        cv2.putText(compare_img, ep_name, (x_start + 10, row * h + 20),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
+        cv2.putText(compare_img, f"qpos[3]={qpos[3]:.2f}", (x_start + 10, y_end - 10),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+
+    # 保存对比图
+    output_path = f"{output_dir}/compare_frame{frame_idx:03d}.png"
+    cv2.imwrite(output_path, compare_img)
+
+    print(f"\n✅ 对比图已保存: {output_path}")
+    print(f"  查看方法: eog {output_path}")
+
+
+if __name__ == "__main__":
+    import sys
+
+    print("="*80)
+    print("特定帧检查工具")
+    print("="*80)
+
+    if len(sys.argv) > 1:
+        frame_idx = int(sys.argv[1])
+        compare_frame_across_episodes(frame_idx=frame_idx, camera='top', num_episodes=10)
+    else:
+        # 默认检查第 0、1、2 帧
+        check_specific_frames(frame_indices=[0, 1, 2], camera='top', num_episodes=10)
+
+        print("\n" + "="*80)
+        print("生成对比图...")
+        print("="*80)
+
+        # 生成第 0 帧的对比图
+        compare_frame_across_episodes(frame_idx=0, camera='top', num_episodes=10)
+        compare_frame_across_episodes(frame_idx=1, camera='top', num_episodes=10)
+        compare_frame_across_episodes(frame_idx=2, camera='top', num_episodes=10)
+
+    print("\n" + "="*80)
+    print("其他用法:")
+    print("  python check_specific_frames.py 0    # 只检查第 0 帧")
+    print("  python check_specific_frames.py 1    # 只检查第 1 帧")
+    print("="*80)
diff --git a/generate_dataset_videos.py b/generate_dataset_videos.py
new file mode 100644
index 0000000..0adae9f
--- /dev/null
+++ b/generate_dataset_videos.py
@@ -0,0 +1,324 @@
+#!/usr/bin/env python3
+"""
+将 HDF5 数据集转换为视频，用于可视化检查
+
+功能：
+1. 将单个 episode 转换为视频
+2. 对比多个 episode 的视频
+3. 放慢播放速度便于观察
+"""
+import os
+import h5py
+import glob
+import cv2
+import numpy as np
+
+
+def episode_to_video(episode_file, output_path, camera='top', fps=30, slow_factor=1):
+    """
+    将单个 episode 转换为视频
+
+    Args:
+        episode_file: HDF5 文件路径
+        output_path: 输出视频路径
+        camera: 要使用的相机名称
+        fps: 帧率
+        slow_factor: 慢放倍数（1=正常，2=半速）
+    """
+    try:
+        with h5py.File(episode_file, 'r') as f:
+            # 读取图像序列
+            img_path = f'/observations/images/{camera}'
+
+            if img_path not in f:
+                print(f"  ❌ 相机 {camera} 不存在")
+                return False
+
+            images = f[img_path][:]  # shape: (T, H, W, C)
+            qpos = f['/observations/qpos'][:]
+            actions = f['/action'][:]
+
+            total_frames = len(images)
+            height, width = images.shape[1], images.shape[2]
+
+            # 创建视频写入器
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            actual_fps = fps // slow_factor
+            out = cv2.VideoWriter(output_path, fourcc, actual_fps, (width, height))
+
+            # 逐帧写入
+            for i in range(total_frames):
+                frame = images[i].astype(np.uint8)
+
+                # 在图像上添加信息
+                info_text = [
+                    f"Episode: {os.path.basename(episode_file).replace('.hdf5', '')}",
+                    f"Frame: {i}/{total_frames}",
+                    f"qpos[0:3]: [{qpos[i, 0]:.2f}, {qpos[i, 1]:.2f}, {qpos[i, 2]:.2f}]",
+                ]
+
+                for j, text in enumerate(info_text):
+                    cv2.putText(frame, text, (10, 30 + j*30),
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+                out.write(frame)
+
+            out.release()
+            print(f"  ✅ 保存: {output_path}")
+            print(f"     帧数: {total_frames}, 尺寸: {width}x{height}, FPS: {actual_fps}")
+            return True
+
+    except Exception as e:
+        print(f"  ❌ 错误: {e}")
+        return False
+
+
+def generate_all_videos(camera='top', num_episodes=5, slow_factor=1):
+    """生成前 N 个 episode 的视频"""
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+
+    if len(episode_files) == 0:
+        print(f"❌ 没有找到数据文件: {dataset_dir}")
+        return
+
+    # 创建输出目录
+    output_dir = '/tmp/dataset_videos'
+    os.makedirs(output_dir, exist_ok=True)
+
+    print(f"找到 {len(episode_files)} 个 episode 文件")
+    print(f"将生成前 {min(num_episodes, len(episode_files))} 个 episode 的视频\n")
+
+    # 生成视频
+    for i in range(min(num_episodes, len(episode_files))):
+        ep_file = episode_files[i]
+        ep_name = os.path.basename(ep_file).replace('.hdf5', '')
+        output_path = f"{output_dir}/{ep_name}_{camera}.mp4"
+
+        print(f"[{i+1}/{min(num_episodes, len(episode_files))}] {ep_name}")
+        episode_to_video(ep_file, output_path, camera=camera, slow_factor=slow_factor)
+        print()
+
+    print(f"✅ 所有视频已保存到: {output_dir}")
+    print(f"\n播放方法:")
+    print(f"  # 播放单个视频")
+    print(f"  vlc {output_dir}/*.mp4")
+    print(f"  ")
+    print(f"  # 或用文件管理器")
+    print(f"  nautilus {output_dir}")
+
+
+def generate_multi_camera_video(episode_idx=0, slow_factor=1):
+    """生成包含多个相机的视频（分屏显示）"""
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+
+    if episode_idx >= len(episode_files):
+        print(f"❌ Episode {episode_idx} 不存在")
+        return
+
+    ep_file = episode_files[episode_idx]
+
+    try:
+        with h5py.File(ep_file, 'r') as f:
+            # 获取所有相机
+            cameras = []
+            for key in f.keys():
+                if 'images' in key:
+                    for cam_name in f[key].keys():
+                        if cam_name not in cameras:
+                            cameras.append(cam_name)
+
+            print(f"Episode {episode_idx} 的相机: {cameras}")
+
+            # 读取所有相机的图像
+            all_images = {}
+            for cam in cameras:
+                img_path = f'/observations/images/{cam}'
+                if img_path in f:
+                    all_images[cam] = f[img_path][:]
+
+            if not all_images:
+                print("❌ 没有找到图像数据")
+                return
+
+            # 获取第一个相机的尺寸
+            first_cam = list(all_images.keys())[0]
+            total_frames = len(all_images[first_cam])
+            height, width = all_images[first_cam].shape[1], all_images[first_cam].shape[2]
+
+            # 创建多相机布局
+            num_cams = len(all_images)
+            cols = min(2, num_cams)
+            rows = (num_cams + cols - 1) // cols
+
+            canvas_width = width * cols
+            canvas_height = height * rows
+
+            # 创建视频写入器
+            output_path = f'/tmp/dataset_videos/episode_{episode_idx}_all_cameras.mp4'
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            out = cv2.VideoWriter(output_path, fourcc, 30 // slow_factor, (canvas_width, canvas_height))
+
+            # 逐帧合成
+            for i in range(total_frames):
+                canvas = np.zeros((canvas_height, canvas_width, 3), dtype=np.uint8)
+
+                for cam_idx, cam_name in enumerate(all_images.keys()):
+                    img = all_images[cam_name][i]
+
+                    # 计算在画布上的位置
+                    row = cam_idx // cols
+                    col = cam_idx % cols
+                    y_start = row * height
+                    y_end = y_start + height
+                    x_start = col * width
+                    x_end = x_start + width
+
+                    # 调整大小（如果需要）
+                    if img.shape[:2] != (height, width):
+                        img = cv2.resize(img, (width, height))
+
+                    # 放到画布上
+                    canvas[y_start:y_end, x_start:x_end] = img
+
+                    # 添加相机名称
+                    cv2.putText(canvas, cam_name, (x_start + 10, y_start + 30),
+                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
+
+                # 添加帧信息
+                cv2.putText(canvas, f"Frame: {i}/{total_frames}", (10, canvas_height - 10),
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+                out.write(canvas)
+
+            out.release()
+            print(f"✅ 保存多相机视频: {output_path}")
+
+    except Exception as e:
+        print(f"❌ 错误: {e}")
+
+
+def compare_episodes(camera='top', slow_factor=2):
+    """并排对比多个 episode 的视频"""
+
+    dataset_dir = "roboimi/demos/dataset/sim_transfer"
+    episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
+
+    # 选择要对比的 episode
+    episodes_to_compare = [0, 1, 2, 3, 4]  # 对比前 5 个
+
+    print(f"对比 Episodes: {episodes_to_compare}")
+
+    # 读取所有 episode 的数据
+    all_data = []
+    for ep_idx in episodes_to_compare:
+        if ep_idx >= len(episode_files):
+            continue
+
+        try:
+            with h5py.File(episode_files[ep_idx], 'r') as f:
+                img_path = f'/observations/images/{camera}'
+                if img_path in f:
+                    all_data.append({
+                        'idx': ep_idx,
+                        'images': f[img_path][:],
+                        'qpos': f['/observations/qpos'][:]
+                    })
+        except:
+            pass
+
+    if len(all_data) == 0:
+        print("❌ 没有数据")
+        return
+
+    # 获取参数
+    first_data = all_data[0]
+    height, width = first_data['images'].shape[1], first_data['images'].shape[2]
+    total_frames = min([d['images'].shape[0] for d in all_data])
+
+    # 创建并排布局
+    num_compare = len(all_data)
+    canvas_width = width * num_compare
+    canvas_height = height
+
+    # 创建视频
+    output_path = f'/tmp/dataset_videos/compare_{camera}.mp4'
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, 30 // slow_factor, (canvas_width, canvas_height))
+
+    print(f"生成对比视频，共 {total_frames} 帧...")
+
+    # 逐帧对比
+    for i in range(total_frames):
+        canvas = np.zeros((canvas_height, canvas_width, 3), dtype=np.uint8)
+
+        for j, data in enumerate(all_data):
+            img = data['images'][i]
+            qpos = data['qpos'][i]
+
+            # 调整大小（如果需要）
+            if img.shape[:2] != (height, width):
+                img = cv2.resize(img, (width, height))
+
+            # 放到画布上
+            x_start = j * width
+            x_end = x_start + width
+            canvas[:, x_start:x_end] = img
+
+            # 添加信息
+            ep_name = f"Ep {data['idx']}"
+            cv2.putText(canvas, ep_name, (x_start + 10, 30),
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+            cv2.putText(canvas, f"qpos[0:3]: [{qpos[0]:.2f}, {qpos[1]:.2f}, {qpos[2]:.2f}]",
+                       (x_start + 10, height - 10),
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+
+        # 添加帧号
+        cv2.putText(canvas, f"Frame: {i}/{total_frames}", (10, canvas_height - 30),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
+
+        out.write(canvas)
+
+        if i % 100 == 0:
+            print(f"  进度: {i}/{total_frames}")
+
+    out.release()
+    print(f"✅ 保存对比视频: {output_path}")
+
+
+if __name__ == "__main__":
+    import sys
+
+    print("="*60)
+    print("数据集视频生成工具")
+    print("="*60)
+
+    if len(sys.argv) > 1:
+        command = sys.argv[1]
+
+        if command == 'compare':
+            # 对比多个 episode
+            camera = sys.argv[2] if len(sys.argv) > 2 else 'top'
+            compare_episodes(camera=camera, slow_factor=2)
+
+        elif command == 'multi':
+            # 多相机视频
+            ep_idx = int(sys.argv[2]) if len(sys.argv) > 2 else 0
+            generate_multi_camera_video(episode_idx=ep_idx, slow_factor=1)
+
+        else:
+            print("未知命令")
+    else:
+        # 默认：生成前 5 个 episode 的视频
+        print("\n生成前 5 个 episode 的视频（top 相机，慢放 2x）...")
+        print("="*60 + "\n")
+        generate_all_videos(camera='top', num_episodes=5, slow_factor=2)
+
+        print("\n" + "="*60)
+        print("其他用法:")
+        print("  python generate_dataset_videos.py compare top    # 对比多个 episode")
+        print("  python generate_dataset_videos.py multi 0        # 多相机视频")
+        print("="*60)