diff --git a/inference_reward_record_20260104_105636/debug_check.json b/inference_reward_record_20260104_105636/debug_check.json
new file mode 100644
index 0000000000000000000000000000000000000000..9064496edc7e2eda40e81f14179a7d5058b5a870
--- /dev/null
+++ b/inference_reward_record_20260104_105636/debug_check.json
@@ -0,0 +1,694 @@
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+}
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diff --git a/inference_reward_record_20260104_105636/episode_0001/metadata.json b/inference_reward_record_20260104_105636/episode_0001/metadata.json
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@@ -0,0 +1,18 @@
+{
+    "info": {
+        "version": "1.0.0",
+        "date": "2026-01-04",
+        "author": "unitree",
+        "image": {
+            "width": 640,
+            "height": 480,
+            "fps": 30.0
+        }
+    },
+    "text": {
+        "goal": "Inference with Human Reward",
+        "desc": "Reward Key: g, Punish Key: b",
+        "steps": "Model execution"
+    },
+    "language_instruction": "Pick up the banana and put it in the basket."
+}
\ No newline at end of file
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+{"timestamp": 1767495830.6987774, "timestamp_str": "2026-01-04 11:03:50", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.194497, 0.056065, 0.294111, -0.5458107296650286, 0.07196401225107504], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001219_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001220_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001221_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1221}}
+{"timestamp": 1767495830.8695354, "timestamp_str": "2026-01-04 11:03:50", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.194039, 0.059459, 0.295509, -0.5494835036856461, 0.06638677341042082], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001222_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001223_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001224_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1224}}
+{"timestamp": 1767495831.5275981, "timestamp_str": "2026-01-04 11:03:51", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.207235, 0.056803, 0.293337, -0.5289300000257893, 0.042975432538516614], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001225_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001226_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001227_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1227}}
+{"timestamp": 1767495831.9537787, "timestamp_str": "2026-01-04 11:03:51", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.201628, 0.052813, 0.294179, -0.5345186191993958, 0.058625944134725716], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001228_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001229_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001230_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1230}}
+{"timestamp": 1767495832.6415687, "timestamp_str": "2026-01-04 11:03:52", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.193329, 0.045346, 0.284255, -0.5686806421226192, 0.10086692485054513], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001231_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001232_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001233_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1233}}
+{"timestamp": 1767495833.2618494, "timestamp_str": "2026-01-04 11:03:53", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.190564, 0.045638, 0.286988, -0.5641828619371089, 0.11292293081164845], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001234_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001235_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001236_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1236}}
+{"timestamp": 1767495833.9084349, "timestamp_str": "2026-01-04 11:03:53", "input_data": {"proprio": {"shape": [20], "dtype": "float64", "sample": [0.189467, 0.047733, 0.288973, -0.5623168569767067, 0.10734633913526445], "type": "eef_6d_new_reg output"}, "images": {"high": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001237_high.jpg", "left_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001238_left_wrist.jpg", "right_wrist": "./data_inference/inference_reward_record_20260104_105636/inference_frames/frame_001239_right_wrist.jpg"}, "language_instruction": "Pick up the banana and put it in the basket.", "steps": 30, "domain_id": 21, "frame_counter": 1239}}
diff --git a/visualize.py b/visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..349b03b160edff1baad79c9d49ba9000dda102b1
--- /dev/null
+++ b/visualize.py
@@ -0,0 +1,148 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+# --- 配置 ---
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# --- 配置 ---
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+# --- 配置 ---
+# ------------
+file_path = 'data_inference/inference_reward_record_20260104_105636/episode_0001/data.jsonl'  # 你的文件名
+
+def visualize_trajectory(filename):
+    timestamps = []
+    # 假设 states 有两个列表，分别对应 state 0 和 state 1
+    states_0 = []
+    states_1 = []
+    # Actions 分别对应 left 和 right
+    actions_left = []
+    actions_right = []
+    # Grippers
+    gripper_left = []
+    gripper_right = []
+    LIMIT = 100
+    print(f"正在读取文件: {filename} ...")
+    try:
+        count = 0
+        with open(filename, 'r') as f:
+            for line in f:
+                d = json.loads(line)
+                timestamps.append(d['timestamp'])
+                
+                # 提取 State 的前3个值 (XYZ)
+                # 注意：根据您的数据结构，states是一个包含两个列表的列表
+                # 这里假设 states[0] 和 states[1] 分别对应两个实体的状态
+                states_0.append(d['states'][0][:3])
+                states_1.append(d['states'][1][:3])
+                
+                # 提取 Action 的前3个值 (XYZ)
+                actions_left.append(d['actions']['left_arm']['pose'][:3])
+                actions_right.append(d['actions']['right_arm']['pose'][:3])
+                
+                # 提取 Gripper
+                gripper_left.append(d['actions']['left_gripper'][0])
+                gripper_right.append(d['actions']['right_gripper'][0])
+                count += 1
+                if count > LIMIT:
+                    break
+    except FileNotFoundError:
+        print(f"错误: 找不到文件 {filename}。请确保文件在当前目录下或路径正确。")
+        return
+
+    # 转换为 numpy 数组方便处理
+    timestamps = np.array(timestamps)
+    # 将时间戳转换为相对时间（从0开始）
+    rel_time = timestamps - timestamps[0]
+    
+    states_0 = np.array(states_0)
+    states_1 = np.array(states_1)
+    actions_left = np.array(actions_left)
+    actions_right = np.array(actions_right)
+    gripper_left = np.array(gripper_left)
+    gripper_right = np.array(gripper_right)
+
+    # 1. 绘制 XYZ 分量对比图 (State vs Action)
+    fig1, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)
+    labels = ['X', 'Y', 'Z']
+    
+    for i in range(3):
+        # 绘制 State
+        axes[i].plot(rel_time, states_0[:, i], label='State[0]', linestyle='--', alpha=0.7)
+        axes[i].plot(rel_time, states_1[:, i], label='State[1]', linestyle='--', alpha=0.7)
+        # 绘制 Action
+        axes[i].plot(rel_time, actions_left[:, i], label='Action Left', linewidth=1.5)
+        axes[i].plot(rel_time, actions_right[:, i], label='Action Right', linewidth=1.5)
+        
+        axes[i].set_ylabel(f'{labels[i]} (m)')
+        axes[i].grid(True, alpha=0.3)
+        if i == 0:
+            axes[i].legend(loc='upper right')
+            axes[i].set_title('XYZ Position: State vs Action')
+
+    axes[2].set_xlabel('Time (s)')
+    plt.tight_layout()
+    plt.savefig('1_xyz_comparison.png')
+    print("已保存: 1_xyz_comparison.png")
+
+    # 2. 绘制 3D 轨迹图
+    fig2 = plt.figure(figsize=(10, 8))
+    ax = fig2.add_subplot(111, projection='3d')
+
+    # 为了图表清晰，这里只画出轨迹，不画时间点
+    ax.plot(states_0[:, 0], states_0[:, 1], states_0[:, 2], label='State[0]', linestyle='--')
+    ax.plot(states_1[:, 0], states_1[:, 1], states_1[:, 2], label='State[1]', linestyle='--')
+    ax.plot(actions_left[:, 0], actions_left[:, 1], actions_left[:, 2], label='Action Left')
+    ax.plot(actions_right[:, 0], actions_right[:, 1], actions_right[:, 2], label='Action Right')
+
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    ax.set_title('3D Trajectories')
+    ax.legend()
+    plt.savefig('2_3d_trajectory.png')
+    print("已保存: 2_3d_trajectory.png")
+
+    # 3. 绘制 Gripper 状态
+    plt.figure(figsize=(12, 4))
+    plt.plot(rel_time, gripper_left, label='Left Gripper')
+    plt.plot(rel_time, gripper_right, label='Right Gripper')
+    plt.xlabel('Time (s)')
+    plt.ylabel('Value')
+    plt.title('Gripper State')
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    plt.savefig('3_gripper.png')
+    print("已保存: 3_gripper.png")
+
+    # 4. 绘制 Timestamp 频率 (Delta Time)
+    plt.figure(figsize=(12, 4))
+    dt = np.diff(timestamps)
+    # 移除异常大的跳跃点以便观察（可选）
+    # dt = dt[dt < 0.5] 
+    
+    plt.plot(rel_time[1:], dt, marker='.', linestyle='-', linewidth=0.5, markersize=3)
+    mean_dt = np.mean(dt)
+    freq = 1.0 / mean_dt if mean_dt > 0 else 0
+    
+    plt.xlabel('Time (s)')
+    plt.ylabel('Delta Time (s)')
+    plt.title(f'Control Frequency Stability (Mean dt: {mean_dt:.4f}s, ~{freq:.1f} Hz)')
+    plt.grid(True, alpha=0.3)
+    plt.savefig('4_frequency.png')
+    print("已保存: 4_frequency.png")
+
+    plt.show()
+
+if __name__ == "__main__":
+    visualize_trajectory(file_path)
\ No newline at end of file