feat: 描述你的具体修改内容

configs/data/KuavoRosbag2Lerobot.yaml

@@ -14,15 +14,15 @@ hydra:  # Hydra 配置文件保存目录，仅供参数检查使用
 
 rosbag:
   mode: normal  # 可选: normal, resume, merge
-  rosbag_dir: /path/to/rosbags # rosbag文件存放的目录。normal / resume 模式需要
-  target_dir: /path/to/target_dir  # 输出父目录；最终数据集会生成在 target_dir/lerobot
+  rosbag_dir: /home/zwl/merge8.4/kuavo-ros-opensource/src/data_challenge_simulator/examples/bags/run_2026-08-22_10-17-34
+  target_dir: /home/zwl/merge8.4/letools_opensource/LeTools-Learning/kuavo_data/6 # 输出父目录；最终数据集会生成在 target_dir/lerobot
   lerobot_dir_resume: /path/to/lerobot_resume  # resume 模式下，已有 lerobot 数据集的目录
   lerobot_dir_merge: 
     ["/path/to/lerobot1",
     "/path/to/lerobot2"]  # merge 模式下，要合并的多个 lerobot 数据集目录列表
 
 dataset:
-  platform_type: "4pro"  # 硬件平台类型，可选: "4pro", "5w" 或 "5"，默认使用 configs/platform/platform_config.yaml 中的 default 值，比赛默认4pro
+  platform_type: "5w"  # 硬件平台类型，可选: "4pro", "5w" 或 "5"，默认使用 configs/platform/platform_config.yaml 中的 default 值，比赛默认4pro
   eef_type: leju_claw # 末端执行器类型，仿真选择：rq2f85, 真机可选：leju_claw,（夹爪） qiangnao，（灵巧手）
   which_arm: both  # 需要哪一只手臂的关节 + 图像数据，可选: left, right, both，注意图像数据会同时包含头部相机图像
 
@@ -31,8 +31,8 @@ dataset:
   train_hz: 10  # 训练数据的采样频率
   main_timeline: head_cam_h # 将哪个相机设为主相机，默认主相机来自：head_cam_h, 可选：wrist_cam_l, wrist_cam_r
   main_timeline_fps: 30 # 主相机的帧率，必须稳定，默认30帧
-  sample_drop: 10 # 丢弃episode前后的10帧
+  sample_drop: 1 # 丢弃episode前后的10帧
 
   resize:
-    width: 848  # 图像缩放宽度，注意请先查看（推荐浏览器foxglove工具）rosbag中图像的原始尺寸，避免放大或缩小带来模糊或拉伸形变等问题
+    width: 640  # 图像缩放宽度，注意请先查看（推荐浏览器foxglove工具）rosbag中图像的原始尺寸，避免放大或缩小带来模糊或拉伸形变等问题
     height: 480  # 图像缩放高度，注意请先查看（推荐浏览器foxglove工具）rosbag中图像的原始尺寸，避免放大或缩小带来模糊或拉伸形变等问题

configs/deploy/deploy.yaml

@@ -1,19 +1,19 @@
 env:  # 推理环境设置
-  inference_env: real  # 选仿真还是真机 option: sim/real
-  platform_type: "5w"  # 机器人型号 option: "4pro"/"5"/"5w"
+  inference_env: sim  # 选仿真还是真机 option: sim/real
+  platform_type: "5w"  # 轮臂 5W/s60-s63 关节布局
   which_arm: both  # 控制左手、右手还是双手 option: both/left/right
-  eef_type: qiangnao  # 夹爪或手型类型  option: leju_claw/qiangnao/rq2f85(仿真)
+  eef_type: leju_claw  # 夹爪或手型类型  option: leju_claw/qiangnao/rq2f85(仿真)
   ros_rate: 10  # 推理频率
   direct_to_wbc: false  # 是否使用direct to wbc模式。
   enable_action_interpolation: true
   control_rate: 100  # WBC插值后的实际控制指令频率
-  image_size: [848, 480]  # 相机缩放尺寸 option: [640, 480](仿真)/[848, 480](真机）
+  image_size: [640, 480]  # 相机缩放尺寸 option: [640, 480](仿真)/[848, 480](真机）
 
 
 inference:  # 推理和评测设置
-  go_bag_path: null    # bag 回放路径
-  policy_type: client  # 要加载的模型类型,外挂模式选择client, 选择client模式后下面的pretrained_path失去作用
-  pretrained_path: /path/to/checkpoint  # 要加载的权重路径。
+  go_bag_path: /home/zwl/merge8.4/kuavo-ros-opensource/src/data_challenge_simulator/examples/bags/run_2026-08-22_10-17-34/grasp_part_001.bag    # bag 回放路径
+  policy_type: act  # 要加载的模型类型,外挂模式选择client, 选择client模式后下面的pretrained_path失去作用
+  pretrained_path: /home/zwl/merge8.4/letools_opensource/LeTools-Learning/outputs/train/act_7/lerobot_grasp_ring_20260822_115555/checkpoints/last
   eval_episodes: 10  # 评测跑多少条
   max_episode_steps: 200  # 每条最多跑多少步
   task_prompt: "Pick and Place"

configs/platform/platform_config.yaml

@@ -2,7 +2,7 @@
 # 定义不同硬件平台（4Pro、5W）的关节索引范围
 
 # 默认硬件类型
-default: "4pro"
+default: "5w"
 
 # 硬件平台定义
 platforms:

configs/train/lerobot/act.yaml

@@ -1,20 +1,20 @@
 # LeRobot ACT 简化配置。
 dataset: 
-  repo_id: "lerobot/your_dataset"  # 数据集名字，用来做标识。
-  root: null  # 数据地址
+  repo_id: "lerobot/grasp_ring"  # 数据集名字，用来做标识。
+  root: /home/zwl/merge8.4/letools_opensource/LeTools-Learning/kuavo_data/6/lerobot
 
 training: 
   resume: false  # 是否从 output_dir 最新 checkpoint 继续训；续训改为 true，并修改 output_dir 为上阶段 run 的产物路径，如 outputs/train/act_YYYYMMDD_HHMMSS。
-  output_dir: "outputs/train/act" # 权重存储路径
-  job_name: "act"  # 实验名。
-  batch_size: 32  
-  steps: 100000  # 总训练步数
-  save_freq: 20000  # 每N步保存一次权重
+  output_dir: "outputs/train/act_7/lerobot_grasp_ring" # 权重存储路径
+  job_name: "act_3/lerobot_grasp_ring"  # 实验名。
+  batch_size: 8 
+  steps: 30000 # 总训练步数
+  save_freq: 10000 # 每N步保存一次权重
   num_workers: 8  
   seed: 1000  
 
 wandb:  # wandb的一些设置
-  enable: true  # 是否启用 wandb。
+  enable: false  # 是否启用 wandb。
   project: "lerobot_act"  # wandb 项目名。
 
 policy: 

kuavo_data/CvtRosbag2Lerobot.py

@@ -375,9 +375,18 @@ def get_array(key, dtype, default_empty=True):
                     # rq2f85_state = rq2f85_state / 0.8
                     # rq2f85_action = rq2f85_action / 255
 
-                if claw_action.size == 0 and qiangnao_action.size == 0:
+                # Prefer /gripper/state (rq2f85) over /leju_claw_state (claw) for state observation
+                # /leju_claw_state has dual-publisher conflict in simulation (percentage vs radians)
+                if rq2f85_state.size > 0:
+                    if kuavo.IS_BINARY:
+                        claw_state = rq2f85_state  # binary: already 0 or 1
+                    else:
+                        # Convert rq2f85_state (normalized by /0.8) to claw scale (normalized by /100)
+                        # rq2f85_norm = (ctrl+100)/200, claw_norm = 1-ctrl/255
+                        # => claw_norm = (355 - 200 * rq2f85_norm) / 255
+                        claw_state = (355.0 - 200.0 * rq2f85_state) / 255.0
+                if claw_action.size == 0 and rq2f85_action.size > 0:
                     claw_action = rq2f85_action
-                    claw_state  = rq2f85_state
 
                 # =========================
                 # 5. 构建最终 state / action

kuavo_data/common/kuavo_dataset.py

@@ -213,6 +213,25 @@ def process_kuavo_arm_traj(msg):
         # radian
         return {"data": np.deg2rad(msg.position), "timestamp": msg.header.stamp.to_sec()}
 
+    @staticmethod
+    def process_two_arm_hand_pose_cmd(msg):
+        """Process /mm/two_arm_hand_pose_cmd message as arm trajectory.
+
+        当 frame=5（关节空间）时，从 left_pose.joint_angles 和 right_pose.joint_angles
+        提取14维手臂关节角（弧度），作为 action.kuavo_arm_traj 的替代数据源。
+
+        Args:
+            msg (kuavo_msgs/twoArmHandPoseCmd): 双臂手部位姿指令
+
+        Returns:
+            Dict: 14维手臂关节角（弧度）+ 时间戳
+        """
+        left_ja = np.array(msg.hand_poses.left_pose.joint_angles, dtype=np.float64)
+        right_ja = np.array(msg.hand_poses.right_pose.joint_angles, dtype=np.float64)
+        arm_traj = np.concatenate([left_ja, right_ja])
+        ts = msg.hand_poses.header.stamp.to_sec()
+        return {"data": arm_traj, "timestamp": ts}
+
     @staticmethod
     def process_claw_state(msg):
         """
@@ -313,8 +332,8 @@ def __init__(self):
                 "msg_process_fn": self._msg_processer.process_joint_state,
             },
             "action.kuavo_arm_traj": {
-                "topic": "/kuavo_arm_traj",
-                "msg_process_fn": self._msg_processer.process_kuavo_arm_traj,
+                "topic": "/joint_cmd",
+                "msg_process_fn": self._msg_processer.process_joint_cmd_extract_arm,
             },
             "action": {
                 "topic": "/joint_cmd",

kuavo_data/lerobot/meta/info.json

@@ -0,0 +1,174 @@
+{
+    "codebase_version": "v3.0",
+    "robot_type": "kuavo-5w",
+    "total_episodes": 30,
+    "total_frames": 3540,
+    "total_tasks": 1,
+    "chunks_size": 1000,
+    "data_files_size_in_mb": 100,
+    "video_files_size_in_mb": 200,
+    "fps": 10,
+    "splits": {
+        "train": "0:30"
+    },
+    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
+    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
+    "features": {
+        "observation.state": {
+            "dtype": "float32",
+            "shape": [
+                16
+            ],
+            "names": {
+                "state_names": [
+                    "zarm_l1_link",
+                    "zarm_l2_link",
+                    "zarm_l3_link",
+                    "zarm_l4_link",
+                    "zarm_l5_link",
+                    "zarm_l6_link",
+                    "zarm_l7_link",
+                    "left_claw",
+                    "zarm_r1_link",
+                    "zarm_r2_link",
+                    "zarm_r3_link",
+                    "zarm_r4_link",
+                    "zarm_r5_link",
+                    "zarm_r6_link",
+                    "zarm_r7_link",
+                    "right_claw"
+                ]
+            }
+        },
+        "action": {
+            "dtype": "float32",
+            "shape": [
+                16
+            ],
+            "names": {
+                "action_names": [
+                    "zarm_l1_link",
+                    "zarm_l2_link",
+                    "zarm_l3_link",
+                    "zarm_l4_link",
+                    "zarm_l5_link",
+                    "zarm_l6_link",
+                    "zarm_l7_link",
+                    "left_claw",
+                    "zarm_r1_link",
+                    "zarm_r2_link",
+                    "zarm_r3_link",
+                    "zarm_r4_link",
+                    "zarm_r5_link",
+                    "zarm_r6_link",
+                    "zarm_r7_link",
+                    "right_claw"
+                ]
+            }
+        },
+        "observation.images.head_cam_h": {
+            "dtype": "video",
+            "shape": [
+                3,
+                480,
+                640
+            ],
+            "names": [
+                "channels",
+                "height",
+                "width"
+            ],
+            "info": {
+                "video.height": 480,
+                "video.width": 640,
+                "video.codec": "av1",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "video.fps": 10,
+                "video.channels": 3,
+                "has_audio": false
+            }
+        },
+        "observation.images.wrist_cam_l": {
+            "dtype": "video",
+            "shape": [
+                3,
+                480,
+                640
+            ],
+            "names": [
+                "channels",
+                "height",
+                "width"
+            ],
+            "info": {
+                "video.height": 480,
+                "video.width": 640,
+                "video.codec": "av1",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "video.fps": 10,
+                "video.channels": 3,
+                "has_audio": false
+            }
+        },
+        "observation.images.wrist_cam_r": {
+            "dtype": "video",
+            "shape": [
+                3,
+                480,
+                640
+            ],
+            "names": [
+                "channels",
+                "height",
+                "width"
+            ],
+            "info": {
+                "video.height": 480,
+                "video.width": 640,
+                "video.codec": "av1",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "video.fps": 10,
+                "video.channels": 3,
+                "has_audio": false
+            }
+        },
+        "timestamp": {
+            "dtype": "float32",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "frame_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "episode_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "task_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        }
+    }
+}