diff --git a/policy_evaluate_ros2.py b/policy_evaluate_ros2.py
new file mode 100644
index 0000000..7bc6e18
--- /dev/null
+++ b/policy_evaluate_ros2.py
@@ -0,0 +1,398 @@
+import threading
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy, DurabilityPolicy
+from cv_bridge import CvBridge
+from geometry_msgs.msg import Twist
+from std_msgs.msg import Float64MultiArray
+from sensor_msgs.msg import Image, JointState
+import collections
+
+import torch
+import numpy as np
+import os, time, logging, pickle, inspect
+from typing import Dict
+from tqdm import tqdm
+from utils.utils import set_seed, save_eval_results
+from configurations.task_configs.config_tools.basic_configer import (
+    basic_parser,
+    get_all_config,
+)
+from policies.common.maker import make_policy
+from envs.common_env import get_image
+import dm_env
+import cv2
+from policy_evaluate import eval_parser, get_ckpt_path
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class Evaluate(Node):
+    def __init__(self):
+        self.running = True
+        Node.__init__(self, 'evaluate_node')
+
+        self.obs=collections.OrderedDict()
+        self.obs["qpos"] = []
+        self.obs["images"] = {}
+        
+        # subscriber: image, qpos
+        self.bridge = CvBridge()
+        # qos_profile = QoSProfile(
+        #     depth=1,  # 设置队列的深度
+        #     reliability=ReliabilityPolicy.RELIABLE,  # 设置为可靠的传输
+        #     history=HistoryPolicy.KEEP_LAST,  # 设置为KEEP_LAST
+        #     durability=DurabilityPolicy.VOLATILE  # 设置为VOLATILE
+        # )
+        self.head_color_suber = self.create_subscription(Image, '/camera/head_camera/color/image_raw', self.head_color_callback, 1)
+        self.head_depth_suber = self.create_subscription(Image, '/camera/head_camera/aligned_depth_to_color/image_raw', self.head_depth_callback, 2)
+        self.left_color_suber = self.create_subscription(Image, '/camera/left_camera/color/image_raw', self.left_color_callback, 1)
+        self.right_color_suber = self.create_subscription(Image, '/camera/right_camera/color/image_raw', self.right_color_callback, 1)
+        self.joint_state_suber = self.create_subscription(JointState, '/joint_states', self.joint_state_callback, 1)
+
+        # publisher: action
+        self.cmd_vel_puber = self.create_publisher(Twist, '/cmd_vel', 1)
+        self.spine_cmd_puber = self.create_publisher(Float64MultiArray, '/spine_forward_position_controller/commands', 1)
+        self.head_cmd_puber = self.create_publisher(Float64MultiArray, '/head_forward_position_controller/commands', 1)
+        self.left_arm_cmd_puber = self.create_publisher(Float64MultiArray, '/left_arm_forward_position_controller/commands', 1)
+        self.right_arm_cmd_puber = self.create_publisher(Float64MultiArray, '/right_arm_forward_position_controller/commands', 1)
+
+    def head_color_callback(self, msg):
+        self.obs["images"]["0"] = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        # self.get_logger().info('Received head color image')
+
+    def head_depth_callback(self, msg):
+        # TODO
+        self.current_depth_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='mono16')
+        # self.get_logger().warn('Received head depth image, Not implemented!')
+
+    def left_color_callback(self, msg):
+        # img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        self.obs["images"]["1"] = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        # self.obs["images"]["0"] = img
+        # self.get_logger().info('Received left color image')
+
+    def right_color_callback(self, msg):
+        self.obs["images"]["2"] = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        # self.get_logger().info('Received right color image')
+
+    def joint_state_callback(self, msg):
+        self.obs["qpos"] = list(msg.position)
+        # self.get_logger().info(f'Received joint state: {len(self.obs["qpos"])}')
+
+    def publish_action(self, action):
+        # tctr_base
+        twist_msg = Twist()
+        twist_msg.linear.x = float(action[0])  # 线速度
+        twist_msg.angular.z = float(action[1])  # 角速度
+        self.cmd_vel_puber.publish(twist_msg)
+
+        # tctr_slide
+        spine_cmd_msg = Float64MultiArray()
+        spine_cmd_msg.data = [float(val) for val in action[2:3]]  # 上半身目标位置 
+        self.spine_cmd_puber.publish(spine_cmd_msg)
+
+        # tctr_head
+        head_cmd_msg = Float64MultiArray()
+        head_cmd_msg.data = [float(val) for val in action[3:5]]  # 头部目标位置
+        self.head_cmd_puber.publish(head_cmd_msg)
+
+        # tctr_left_arm
+        left_arm_cmd_msg = Float64MultiArray()
+        left_arm_cmd_msg.data = [float(val) for val in action[5:12]]  # 左臂目标位置命令
+        self.left_arm_cmd_puber.publish(left_arm_cmd_msg)
+
+        # tctr_right_arm
+        right_arm_cmd_msg = Float64MultiArray()
+        right_arm_cmd_msg.data = [float(val) for val in action[12:19]]  # 右臂目标位置命令
+        self.right_arm_cmd_puber.publish(right_arm_cmd_msg)
+
+
+    def evaluate(self,args):
+        all_config = get_all_config(args, "eval")
+        set_seed(all_config["seed"])
+        ckpt_names = all_config["ckpt_names"]
+
+        results = []
+
+        # multiple ckpt evaluation
+        for ckpt_name in ckpt_names:
+            success_rate, avg_return = self.eval_bc(all_config, ckpt_name)
+            results.append([ckpt_name, success_rate, avg_return])
+
+        for ckpt_name, success_rate, avg_return in results:
+            logger.info(f"{ckpt_name}: {success_rate=} {avg_return=}")
+
+    def eval_bc(self,config, ckpt_name):
+        # TODO: eval only contains the logic, data flow and visualization
+        # remove other not general processing code outside in policy and env maker
+        # 显式获得配置
+        ckpt_dir = config["ckpt_dir"]
+        stats_path = config["stats_path"]
+        save_dir = config["save_dir"]
+        max_timesteps = config["max_timesteps"]
+        camera_names = config["camera_names"]
+        max_rollouts = config["num_rollouts"]
+        policy_config: dict = config["policy_config"]
+        state_dim = policy_config["state_dim"]
+        action_dim = policy_config["action_dim"]
+        temporal_agg = policy_config["temporal_agg"]
+        num_queries = policy_config["num_queries"]  # i.e. chunk_size
+        dt = 1 / config["fps"]
+        image_mode = config.get("image_mode", 0)
+        save_all = config.get("save_all", False)
+        save_time_actions = config.get("save_time_actions", False)
+        filter_type = config.get("filter", None)
+        ensemble: dict = config.get("ensemble", None)
+        save_dir = save_dir if save_dir != "AUTO" else ckpt_dir
+        result_prefix = "result_" + ckpt_name.split(".")[0]
+        debug = config.get("debug", False)
+        if debug:
+            logger.setLevel(logging.DEBUG)
+            from utils.visualization.ros1_logger import LoggerROS1
+
+            ros1_logger = LoggerROS1("eval_debuger")
+
+        # TODO: remove this
+        ckpt_path = get_ckpt_path(ckpt_dir, ckpt_name, stats_path)
+        policy_config["ckpt_path"] = ckpt_path
+
+        # make and configure policies
+        policies: Dict[str, list] = {}
+        if ensemble is None:
+            logger.info("policy_config:", policy_config)
+            # if ensemble is not None:
+            policy_config["max_timesteps"] = max_timesteps  # TODO: remove this
+            policy = make_policy(policy_config, "eval")
+            policies["Group1"] = (policy,)
+        else:
+            logger.info("ensemble config:", ensemble)
+            ensembler = ensemble.pop("ensembler")
+            for gr_name, gr_cfgs in ensemble.items():
+                policies[gr_name] = []
+                for index, gr_cfg in enumerate(gr_cfgs):
+
+                    policies[gr_name].append(
+                        make_policy(
+                            gr_cfg["policies"][index]["policy_class"],
+                        )
+                    )
+
+        # add action filter
+        # TODO: move to policy maker as wrappers
+        if filter_type is not None:
+            # init filter
+            from OneEuroFilter import OneEuroFilter
+
+            config = {
+                "freq": config["fps"],  # Hz
+                "mincutoff": 0.01,  # Hz
+                "beta": 0.05,
+                "dcutoff": 0.5,  # Hz
+            }
+            filters = [OneEuroFilter(**config) for _ in range(action_dim)]
+
+        # init pre/post process functions
+        # TODO: move to policy maker as wrappers
+        use_stats = True
+        if use_stats:
+            with open(stats_path, "rb") as f:
+                stats = pickle.load(f)
+            pre_process = lambda s_qpos: (s_qpos - stats["qpos_mean"]) / stats["qpos_std"]
+            post_process = lambda a: a * stats["action_std"] + stats["action_mean"]
+        else:
+            pre_process = lambda s_qpos: s_qpos
+            post_process = lambda a: a
+
+        showing_images = config.get("show_images", False)
+
+        def show_images(ts):
+            images: dict = ts.observation["images"]
+            for name, value in images.items():
+                # logger.info(f"Showing {name}: {value}...")
+                cv2.imshow(name, value)
+                # cv2.imwrite(f"{name}.png", value)
+            cv2.waitKey(1)
+
+        # evaluation loop
+        if hasattr(policy, "eval"):
+            policy.eval()
+        env_max_reward = 0
+        episode_returns = []
+        highest_rewards = []
+        num_rollouts = 0
+        policy_sig = inspect.signature(policy).parameters
+        prediction_freq = 100000
+        for rollout_id in range(max_rollouts):
+
+            # evaluation loop
+            all_time_actions = torch.zeros(
+                [max_timesteps, max_timesteps + num_queries, action_dim]
+            ).cpu()
+
+            qpos_history = torch.zeros((1, max_timesteps, state_dim)).cpu()
+            image_list = []  # for visualization
+            qpos_list = []
+            action_list = []
+            rewards = []
+            with torch.inference_mode():
+                if showing_images:
+                    # must show enough times to clear the black screen
+                    for _ in range(10):
+                        show_images(ts)
+                logger.info(f"Current rollout: {rollout_id} for {ckpt_name}.")
+                v = input(f"Press Enter to start evaluation or z and Enter to exit...")
+                if v == "z":
+                    self.running = False
+                    break
+
+                #zshtodo
+                self.ts = dm_env.TimeStep(
+                    step_type=dm_env.StepType.FIRST,
+                    reward=0,
+                    discount=None,
+                    observation=self.obs,
+                )
+
+                if hasattr(policy, "reset"):
+                    policy.reset()
+                try:
+                    for t in tqdm(range(max_timesteps)):
+                        start_time = time.time()
+                        image_list.append(self.ts.observation["images"])
+                        if showing_images:
+                            show_images(self.ts)
+                        curr_image = get_image(self.ts, camera_names, image_mode)
+                        qpos_numpy = np.array(self.ts.observation["qpos"])
+
+                        logger.debug(f"raw qpos: {qpos_numpy}")
+                        qpos = pre_process(qpos_numpy)  # normalize qpos
+                        logger.debug(f"pre qpos: {qpos}")
+                        qpos = torch.from_numpy(qpos).float().cpu().unsqueeze(0)
+                        qpos_history[:, t] = qpos
+
+                        logger.debug(f"observe time: {time.time() - start_time}")
+                        start_time = time.time()
+                        # wrap policy
+                        target_t = t % num_queries
+                        if temporal_agg or target_t == 0:
+                            # (1, chunk_size, 7) for act
+                            all_actions: torch.Tensor = policy(qpos, curr_image)
+                        all_time_actions[[t], t : t + num_queries] = all_actions
+                        index = 0 if temporal_agg else target_t
+                        raw_action = all_actions[:, index]
+
+                        # post-process predicted action
+                        # dim: (1,7) -> (7,)
+                        raw_action = raw_action.squeeze(0).cpu().numpy()
+                        logger.debug(f"raw action: {raw_action}")
+                        action = post_process(raw_action)  # de-normalize action
+                        # logger.debug(f"post action: {action}")
+                        if filter_type is not None:  # filt action
+                            for i, filter in enumerate(filters):
+                                action[i] = filter(action[i], time.time())
+                        # limit the prediction frequency
+                        time.sleep(max(0, 1 / prediction_freq - (time.time() - start_time)))
+                        logger.debug(f"prediction time: {time.time() - start_time}")
+                        # step the environment
+                        if debug:
+                            # dt = 1
+                            ros1_logger.log_1D("joint_position", list(qpos_numpy))
+                            ros1_logger.log_1D("joint_action", list(action))
+                            for name, image in self.ts.observation["images"].items():
+                                ros1_logger.log_2D("image_" + name, image)
+                        #zshtodo ?
+                        # ts: dm_env.TimeStep = env.step(action, sleep_time=dt)
+                        # publish action:
+                        self.publish_action(action)
+
+                        # for visualization
+                        qpos_list.append(qpos_numpy)
+                        action_list.append(action)
+                        rewards.append(self.ts.reward)
+                        # debug
+                        # input(f"Press Enter to continue...")
+                        # break
+                except KeyboardInterrupt:
+                    logger.info(f"Current roll out: {rollout_id} interrupted by CTRL+C...")
+                    continue
+                else:
+                    num_rollouts += 1
+
+            rewards = np.array(rewards)
+            episode_return = np.sum(rewards[rewards != None])
+            episode_returns.append(episode_return)
+            episode_highest_reward = np.max(rewards)
+            highest_rewards.append(episode_highest_reward)
+            logger.info(
+                f"Rollout {rollout_id}\n{episode_return=}, {episode_highest_reward=}, {env_max_reward=}, Success: {episode_highest_reward==env_max_reward}"
+            )
+
+            # saving evaluation results
+            if save_dir != "":
+                dataset_name = f"{result_prefix}_{rollout_id}"
+                save_eval_results(
+                    save_dir,
+                    dataset_name,
+                    rollout_id,
+                    image_list,
+                    qpos_list,
+                    action_list,
+                    camera_names,
+                    dt,
+                    all_time_actions,
+                    save_all=save_all,
+                    save_time_actions=save_time_actions,
+                )
+        if num_rollouts > 0:
+            success_rate = np.mean(np.array(highest_rewards) == env_max_reward)
+            avg_return = np.mean(episode_returns)
+            summary_str = (
+                f"\nSuccess rate: {success_rate}\nAverage return: {avg_return}\n\n"
+            )
+            for r in range(env_max_reward + 1):
+                more_or_equal_r = (np.array(highest_rewards) >= r).sum()
+                more_or_equal_r_rate = more_or_equal_r / num_rollouts
+                summary_str += f"Reward >= {r}: {more_or_equal_r}/{num_rollouts} = {more_or_equal_r_rate*100}%\n"
+
+            logger.info(summary_str)
+
+            # save success rate to txt
+            if save_dir != "":
+                with open(os.path.join(save_dir, dataset_name + ".txt"), "w") as f:
+                    f.write(summary_str)
+                    f.write(repr(episode_returns))
+                    f.write("\n\n")
+                    f.write(repr(highest_rewards))
+                logger.info(
+                    f'Success rate and average return saved to {os.path.join(save_dir, dataset_name + ".txt")}'
+                )
+        else:
+            success_rate = 0
+            avg_return = 0
+        if showing_images:
+            cv2.destroyAllWindows()
+        return success_rate, avg_return
+
+
+
+if __name__ == "__main__":
+    parser = basic_parser()
+    eval_parser(parser)
+    args = parser.parse_args()
+    args_dict = vars(args)
+
+    rclpy.init()
+    np.set_printoptions(precision=3, suppress=True, linewidth=500)
+
+    exec_node = Evaluate()
+    spin_thread = threading.Thread(target=lambda:rclpy.spin(exec_node))
+    spin_thread.start()
+
+    while rclpy.ok() and exec_node.running:
+        exec_node.evaluate(args_dict)
+
+    exec_node.destroy_node()
+    rclpy.shutdown()
+    spin_thread.join()