update

benchmark.yaml

@@ -1,77 +1,80 @@
 general:
   scan_project: true
   root_paths:
-    asset: /home/ubuntu/projects/gen_data/data
+    asset: /home/ubuntu/xionghao/sim_hofee/sim_hofee/assets
-    checkpoints: /home/ubuntu/data/models
+    checkpoints: /home/ubuntu/xionghao/starVLA-starVLA/playground/Checkpoints
-    output: /home/ubuntu/output
+    output: /home/ubuntu/xionghao/sim_hofee
 
 simulation:
+  stereotype: isaaclab
+  intiailize_steps: 300
   launch_config:
     device: cuda
     enable_cameras: true
     headless: false
     livestream: 0
     
+    
 scene:
-  name: kujiale_multispace
+  name: 827313_home
   base_config:
     stereotype: usd
-    name: _827313_home_workspace_00
+    name: _827313_home_workspace_01
     source: local
-    asset_path: asset://scenes/kujiale_multispace/827313_home/workspace_00.usd
+    asset_path: asset://scenes/kujiale_multispace/827313_home/workspace_01.usd
   object_cfg_dict:
-    omni6DPose_can_016:
+    omni6DPose_timer_017:
-      name: omni6DPose_can_016
+      name: omni6DPose_timer_017
       stereotype: rigid
       source: local
-      asset_path: asset://objects/omni6DPose/can/omni6DPose_can_016/Aligned.usd
+      asset_path: asset://objects/omni6DPose/timer/omni6DPose_timer_017/Aligned.usd
       scale:
       - 0.001
       - 0.001
       - 0.001
       position:
-      - 0.2
+      - 0.552364
-      - -4.15243
+      - -4.0582599999999995
-      - 0.5
+      - 0.524713118
       quaternion:
-      - -0.304408012043137
+      - 0.166210542394157
-      - -0.304408012043137
+      - 0.166210542394157
-      - 0.638228612805745
+      - 0.6872947370648492
-      - 0.6382286128057448
+      - 0.6872947370648491
       axis_y_up: true
-    omni6DPose_plug_001:
+    omni6DPose_book_031:
-      name: omni6DPose_plug_001
+      name: omni6DPose_book_031
       stereotype: rigid
       source: local
-      asset_path: asset://objects/omni6DPose/plug/omni6DPose_plug_001/Aligned.usd
+      asset_path: asset://objects/omni6DPose/book/omni6DPose_book_031/Aligned.usd
       scale:
       - 0.001
       - 0.001
       - 0.001
       position:
-      - 0.219859
+      - 0.6623640000000001
-      - -3.852430000000001
+      - -3.7882599999999997
-      - 0.510259093
+      - 0.5101601435
       quaternion:
-      - 0.7070997233636068
+      - 0.7063055546421202
-      - 0.707099723363607
+      - 0.7063055546421203
-      - 0.003159306745230588
+      - -0.03365209475927027
-      - 0.003159306745230588
+      - -0.033652094759270265
       axis_y_up: true
   robot_cfg_dict:
-    Franka:
+    Franka_Robotiq_2f85:
-      name: Franka
+      name: Franka_Robotiq_2f85
       asset_path: asset://Franka/franka_robotiq_2f85_zedmini.usd
       position:
-      - -0.3779152929316859
+      - 1.082364
-      - -3.943187336951741
+      - -3.92826
-      - 0.3130090550954169
+      - 0.47629299999999997
-      rotation:
+      rotation: 
-      - 0.9987576855319703
+      - 7.549799991308018e-08
       - 0.0
       - 0.0
-      - 0.04983056883903615
+      - 0.9999999999999973
-      stereotype: single_gripper_arm_robot
+      stereotype: modular_robot
       source: local
       ee_link_name: panda_link8
       ik_joint_names:
@@ -83,18 +86,43 @@ scene:
         - panda_joint6
         - panda_joint7
       init_joint_position:
+        # panda_joint1: 0.18641542
+        # panda_joint2: 0.47660449
+        # panda_joint3: -0.03320411
+        # panda_joint4: -2.27693725
+        # panda_joint5: 0.98161776
+        # panda_joint6: 2.20247197
+        # panda_joint7: 0.71794897
         panda_joint2: -0.1633
         panda_joint4: -1.07
         panda_joint6: 0.8933
         panda_joint7: 0.785
-      arm_actuator_name: franka_arm
+      arm_modules:
-      gripper_actuator_name: robotiq_2f_85
+        main_arm:
-      use_planner: true
+          arm_actuator_name: franka_arm
-      planner_cfg:
+          ee_link_name: panda_link8
-        stereotype: curobo
+          ee_type: gripper
-        lazy_init: true
+          ee_actuator_name: robotiq_gripper
-        robot_config_file: asset://curobo/franka_robotiq_2f85/franka_robotiq_2f85.yml
+      actuator_cfg_dict:
-        world_config_source: stage
+        franka_arm:
+          stereotype: arm
+          joint_names_expr: [panda_joint1, panda_joint2, panda_joint3, panda_joint4, panda_joint5, panda_joint6, panda_joint7]
+          stiffness: 3000.0
+          damping: 800.0
+        robotiq_gripper:
+          stereotype: gripper
+          joint_names_expr: [robotiq_85_left_knuckle_joint]
+          stiffness: 10000
+          damping: 500.0
+          close_control_type: velocity
+          open_control_type: position
+          drive_joints:
+            robotiq_85_left_knuckle_joint:
+              close_velocity: 5.0
+              open_velocity: -5.0
+              close_position: 0.8
+              open_position: 0.0
+      use_planner: false
   sensor_cfg_dict:
     Hand_Camera:
       name: Hand_Camera
@@ -108,23 +136,23 @@ scene:
       camera_model: pinhole
       fix_camera: true
       focal_length: 2.8
-      horizontal_aperture: 4.890881131191918
+      horizontal_aperture: 4.893416860031241
       vertical_aperture: 2.7608816125932627
       convention: opengl
       attach_to:
-        target_name: Franka
+        target_name: Franka_Robotiq_2f85
         is_articulation_part: true
         articulation_part_name: panda_link8
         create_fixed_joint: true
         local_position:
-        - -0.07176474936469446
+        - -0.07128738160694643
-        - 0.02890958100382394
+        - 0.03551506300731732
-        - 0.01978286477078585
+        - 0.018927748370281355
         local_rotation:
-        - 0.12352531576657892
+        - -0.12117023430710862
-        - 0.7000519980574638
+        - -0.6862313269668
-        - -0.6933396337721066
+        - 0.7070213671685396
-        - -0.11810524383481495
+        - 0.12052023305019997
     Left_Camera:
       name: Left_Camera
       stereotype: camera
@@ -137,20 +165,20 @@ scene:
       camera_model: pinhole
       fix_camera: false
       focal_length: 2.1
-      horizontal_aperture: 5.019302546405283
+      horizontal_aperture: 5.030789363390793
       vertical_aperture: 2.833796298140747
       convention: opengl
       attach_to:
-        target_name: Franka
+        target_name: Franka_Robotiq_2f85
         local_position:
-        - -0.07383269512283744
+        - 0.31702696813014064
-        - -0.4566116797983716
+        - -0.3844238699868664
-        - 0.5664518136443555
+        - 0.6551552990137672
         local_rotation:
-        - 0.7669289562134721
+        - 0.8742457685173938
-        - 0.4437472984144337
+        - 0.38378563025938384
-        - -0.232167881174614
+        - -0.11951449178007277
-        - -0.4012560108236337
+        - -0.27224843891267797
     Right_Camera:
       name: Right_Camera
       stereotype: camera
@@ -163,20 +191,20 @@ scene:
       camera_model: pinhole
       fix_camera: false
       focal_length: 2.1
-      horizontal_aperture: 5.053278483887542
+      horizontal_aperture: 5.050364265142387
       vertical_aperture: 2.833796298140747
       convention: opengl
       attach_to:
-        target_name: Franka
+        target_name: Franka_Robotiq_2f85
         local_position:
-        - 0.4501524009531093
+        - 0.21844487914880717
-        - 0.7206899873248545
+        - 0.20172329179193413
-        - 0.27525652672526973
+        - 0.30108042236545296
         local_rotation:
-        - 0.007056820167439314
+        - -0.5316249212230874
-        - 0.007107689842383092
+        - -0.38697158527836417
-        - 0.709606075042274
+        - 0.44338617110944967
-        - 0.7045274304789896
+        - 0.6091277686910994
 
 extension:
   extension_cfg_dict:
@@ -185,9 +213,10 @@ extension:
       stereotype: data_collect
       observer_cfgs:
         - stereotype: robot_observer
-          name: Franka
+          name: Franka_Robotiq_2f85
+          target_joint_names: [panda_joint1, panda_joint2, panda_joint3, panda_joint4, panda_joint5, panda_joint6, panda_joint7, robotiq_85_left_knuckle_joint]
           observe_ee_pose: true
-          observe_gripper_drive_state: true
+          observe_ee_state: true
           observe_joint_position: true
           observe_joint_velocity: true
           observe_joint_positions: true
@@ -210,22 +239,23 @@ extension:
       stereotype: benchmark
       data_collector_name: benchmark_data_collect
       action_frequency: 15.0
-      timeout_per_episode: 30
+      timeout_per_episode: 300
       goals:
         - name: cola on top of book
           description: check if the cola bottle is on the book
           stereotype: on_top
-          object_A_name: omni6DPose_plug_001
+          object_A_name: omni6DPose_book_031
-          object_B_name: omni6DPose_can_016
+          object_B_name: omni6DPose_timer_017
       policy:
         stereotype: starvla
-        robot_name: Franka
+        robot_name: Franka_Robotiq_2f85
+        arm_name: main_arm
         sensor_names: [Hand_Camera, Left_Camera, Right_Camera]
-        prompt: pick up the can
+        prompt: pick up the timer and put on the book
         run_trunk_size: 16
         gripper_width_mapper_file: ./gripper_width_robotiq_2f85_fixed.json
-        visualize_action_ee_pose: false
+        visualize_action_ee_pose: true
-        visualize_state_ee_pose: false
+        visualize_state_ee_pose: true
         visualize_bounding_box_targets: [] # [omni6DPose_plug_001, omni6DPose_can_016] # 打开会被policy看到，会影响policy的推理结果
 
     recorder:
@@ -237,7 +267,10 @@ extension:
       postprocess_list: ["hdf5", "video"]
 
 policy_server:
-  ckpt_path: checkpoints://0318_qwenpi_droid_pretrain_8node/checkpoints/steps_30000_pytorch_model.pt
+  # ckpt_path: checkpoints://0324_qwenpi_droid_pretrain_8node/checkpoints/steps_30000_pytorch_model.pt
+  # ckpt_path: checkpoints://0405_qwenpi_droid_norm_pretrain_8node/checkpoints/steps_60000_pytorch_model.pt
+  # ckpt_path: checkpoints://0407_qwenpi_droid_postrain/final_model/pytorch_model.pt
+  ckpt_path: checkpoints://0407_qwenpi_droid_from_scratch/final_model/pytorch_model.pt
   ckpt_source: local
   host: 0.0.0.0
   port: 5000

benchmark_can.yaml

@@ -0,0 +1,257 @@
+general:
+  scan_project: true
+  root_paths:
+    asset: /home/ubuntu/xionghao/sim_hofee/sim_hofee/assets
+    checkpoints: /home/ubuntu/xionghao/starVLA-starVLA/playground/Checkpoints
+    output: /home/ubuntu/xionghao/sim_hofee
+
+simulation:
+  launch_config:
+    device: cuda
+    enable_cameras: true
+    headless: false
+    livestream: 0
+    
+scene:
+  name: kujiale_multispace
+  base_config:
+    stereotype: usd
+    name: _827313_home_workspace_00
+    source: local
+    asset_path: asset://scenes/kujiale_multispace/827313_home/workspace_00.usd
+  object_cfg_dict:
+    omni6DPose_can_016:
+      name: omni6DPose_can_016
+      stereotype: rigid
+      source: local
+      asset_path: asset://objects/omni6DPose/can/omni6DPose_can_016/Aligned.usd
+      scale:
+      - 0.001
+      - 0.001
+      - 0.001
+      position:
+      - 0.2
+      - -4.05243
+      - 0.5
+      quaternion:
+      - -0.304408012043137
+      - -0.304408012043137
+      - 0.638228612805745
+      - 0.6382286128057448
+      axis_y_up: true
+    omni6DPose_can_011:
+      name: omni6DPose_can_011
+      stereotype: rigid
+      source: local
+      asset_path: asset://objects/omni6DPose/can/omni6DPose_can_011/Aligned.usd
+      scale:
+      - 0.001
+      - 0.001
+      - 0.001
+      position:
+      - 0.219859
+      - -3.82430000000001
+      - 0.510259093
+      quaternion:
+      - 0.7070997233636068
+      - 0.707099723363607
+      - 0.003159306745230588
+      - 0.003159306745230588
+      axis_y_up: true
+  robot_cfg_dict:
+    Franka:
+      name: Franka
+      asset_path: asset://Franka/franka_robotiq_2f85_zedmini.usd
+      position:
+      - -0.3779152929316859
+      - -3.943187336951741
+      - 0.3130090550954169
+      rotation:
+      - 0.9987576855319703
+      - 0.0
+      - 0.0
+      - 0.04983056883903615
+      stereotype: single_gripper_arm_robot
+      source: local
+      ee_link_name: panda_link8
+      ik_joint_names:
+        - panda_joint1
+        - panda_joint2
+        - panda_joint3
+        - panda_joint4
+        - panda_joint5
+        - panda_joint6
+        - panda_joint7
+      init_joint_position:
+        panda_joint2: -0.1633
+        panda_joint4: -1.07
+        panda_joint6: 0.8933
+        panda_joint7: 0.785
+        # panda_joint1: 0.00201746
+        # panda_joint2: -0.25797674
+        # panda_joint3: -0.02563748
+        # panda_joint4: -1.68997145
+        # panda_joint5: -0.0735122
+        # panda_joint6: 1.45765436
+        # panda_joint7: 0.56564939
+      arm_actuator_name: franka_arm
+      gripper_actuator_name: robotiq_2f_85
+      use_planner: true
+      planner_cfg:
+        stereotype: curobo
+        lazy_init: true
+        robot_config_file: asset://curobo/franka_robotiq_2f85/franka_robotiq_2f85.yml
+        world_config_source: stage
+  sensor_cfg_dict:
+    Hand_Camera:
+      name: Hand_Camera
+      stereotype: camera
+      data_types:
+      - rgb
+      - depth
+      - normals
+      width: 1280
+      height: 720
+      camera_model: pinhole
+      fix_camera: true
+      focal_length: 2.8
+      horizontal_aperture: 4.890881131191918
+      vertical_aperture: 2.7608816125932627
+      convention: opengl
+      attach_to:
+        target_name: Franka
+        is_articulation_part: true
+        articulation_part_name: panda_link8
+        create_fixed_joint: true
+        local_position:
+        - -0.07176474936469446
+        - 0.02890958100382394
+        - 0.01978286477078585
+        local_rotation:
+        - 0.12352531576657892
+        - 0.7000519980574638
+        - -0.6933396337721066
+        - -0.11810524383481495
+    Left_Camera:
+      name: Left_Camera
+      stereotype: camera
+      data_types:
+      - rgb
+      - depth
+      - normals
+      width: 1280
+      height: 720
+      camera_model: pinhole
+      fix_camera: false
+      focal_length: 2.1
+      horizontal_aperture: 5.019302546405283
+      vertical_aperture: 2.833796298140747
+      convention: opengl
+      attach_to:
+        target_name: Franka
+        local_position:
+        - -0.07383269512283744
+        - -0.4566116797983716
+        - 0.5664518136443555
+        local_rotation:
+        - 0.7669289562134721
+        - 0.4437472984144337
+        - -0.232167881174614
+        - -0.4012560108236337
+    Right_Camera:
+      name: Right_Camera
+      stereotype: camera
+      data_types:
+      - rgb
+      - depth
+      - normals
+      width: 1280
+      height: 720
+      camera_model: pinhole
+      fix_camera: false
+      focal_length: 2.1
+      horizontal_aperture: 5.053278483887542
+      vertical_aperture: 2.833796298140747
+      convention: opengl
+      attach_to:
+        target_name: Franka
+        local_position:
+        - 0.4501524009531093
+        - 0.7206899873248545
+        - 0.27525652672526973
+        local_rotation:
+        - 0.007056820167439314
+        - 0.007107689842383092
+        - 0.709606075042274
+        - 0.7045274304789896
+
+extension:
+  extension_cfg_dict:
+    benchmark_data_collect:
+      enable: true
+      stereotype: data_collect
+      observer_cfgs:
+        - stereotype: robot_observer
+          name: Franka
+          target_joint_names: [panda_joint1, panda_joint2, panda_joint3, panda_joint4, panda_joint5, panda_joint6, panda_joint7]
+          observe_ee_pose: true
+          observe_gripper_drive_state: true
+          observe_joint_position: true
+          observe_joint_velocity: true
+          observe_joint_positions: true
+          observe_joint_velocities: true
+          observe_joint_accelerations: true
+          observe_joint_position_targets: true
+          observe_joint_velocity_targets: true
+        - stereotype: sensor_observer
+          name: Hand_Camera
+          observe_rgb: true
+        - stereotype: sensor_observer
+          name: Left_Camera
+          observe_rgb: true
+        - stereotype: sensor_observer
+          name: Right_Camera
+          observe_rgb: true
+          
+    starvla_benchmark:
+      enable: true
+      stereotype: benchmark
+      data_collector_name: benchmark_data_collect
+      action_frequency: 15.0
+      timeout_per_episode: 300
+      goals:
+        - name: cola on top of book
+          description: check if the cola bottle is on the book
+          stereotype: on_top
+          object_A_name: omni6DPose_can_016
+          object_B_name: omni6DPose_can_011
+      policy:
+        stereotype: starvla
+        robot_name: Franka
+        sensor_names: [Hand_Camera, Left_Camera, Right_Camera]
+        prompt: pick up the red can on the table
+        run_trunk_size: 16
+        gripper_width_mapper_file: ./gripper_width_robotiq_2f85_fixed.json
+        visualize_action_ee_pose: false
+        visualize_state_ee_pose: false
+        visualize_bounding_box_targets: [] # [omni6DPose_plug_001, omni6DPose_can_016] # 打开会被policy看到，会影响policy的推理结果
+
+    recorder:
+      enable: true # set to true to record the data
+      stereotype: record
+      data_collector_name: benchmark_data_collect
+      record_fps: 30
+      backend_root_path: output://benchmark_record
+      postprocess_list: ["hdf5", "video"]
+
+policy_server:
+  # ckpt_path: checkpoints://0324_qwenpi_droid_pretrain_8node/checkpoints/steps_30000_pytorch_model.pt
+  # ckpt_path: checkpoints://0401_qwenpi_droid_pretrain_8node/checkpoints/steps_20000_pytorch_model.p
+  ckpt_path: checkpoints://0405_qwenpi_droid_norm_pretrain_8node/checkpoints/steps_60000_pytorch_model.pt
+  # ckpt_path: checkpoints://0407_qwenpi_droid_postrain/checkpoints/steps_10000_pytorch_model.pt
+  ckpt_source: local
+  host: 0.0.0.0
+  port: 5000
+  use_bf16: true
+  unnorm_key: oxe_bridge
+  state_mode: ee_pose7

starvla_inference_server.py

@@ -12,6 +12,14 @@ import cv2
 from flask import Flask, request, Response
 from PIL import Image
 
+def pad_to_dim(x: np.ndarray, target_dim: int, axis: int = -1, value: float = 0.0) -> np.ndarray:
+    """Pad an array to the target dimension with zeros along the specified axis."""
+    current_dim = x.shape[axis]
+    if current_dim < target_dim:
+        pad_width = [(0, 0)] * len(x.shape)
+        pad_width[axis] = (0, target_dim - current_dim)
+        return np.pad(x, pad_width, constant_values=value)
+    return x
 
 class StarvlaInferenceServer:
 
@@ -19,7 +27,7 @@ class StarvlaInferenceServer:
 
         with open(config_path, "r") as f:
             cfg = yaml.safe_load(f)
-
+        
         policy_server_cfg = cfg["policy_server"]
         root_paths = cfg["general"]["root_paths"]
         self.ckpt_source = policy_server_cfg["ckpt_source"]
@@ -88,14 +96,15 @@ class StarvlaInferenceServer:
         img_wrist = Image.fromarray(cv2.resize(wrist_rgb, target_size))
 
         state_vec = obs["state"]
-
+        # import ipdb;ipdb.set_trace()
+        # state_vec = pad_to_dim(np.array(state_vec), 100, axis=-1)
         return img_left, img_right, img_wrist, state_vec, obs["prompt"]
 
     def inference(self, observation: dict) -> dict:
-
+        
         img_left, img_right, img_wrist, state_vec, prompt = \
             self.parse_observation(observation)
-
+        print(f"{state_vec.shape}")
         vla_input = {
             "batch_images": [[img_left, img_right, img_wrist]],
             "instructions": [prompt],

starvla_inference_server_unnorm.py

@@ -0,0 +1,257 @@
+
+import yaml
+import pickle
+import argparse
+import os
+import traceback
+from urllib.parse import urlparse
+import numpy as np
+import torch
+import cv2
+import json
+from flask import Flask, request, Response
+from PIL import Image
+from pathlib import Path
+
+
+def pad_to_dim(x: np.ndarray, target_dim: int, axis: int = -1, value: float = 0.0) -> np.ndarray:
+    """Pad an array to the target dimension with zeros along the specified axis."""
+    current_dim = x.shape[axis]
+    if current_dim < target_dim:
+        pad_width = [(0, 0)] * len(x.shape)
+        pad_width[axis] = (0, target_dim - current_dim)
+        return np.pad(x, pad_width, constant_values=value)
+    return x
+
+def normalize_eepose_state(raw_state, stats, start_idx, norm_len=3):
+    """
+    根据统计信息对原始动作的特定切片进行归一化 (Normalize)，映射到 [-1.0, 1.0] 区间
+    
+    :param raw_action: 包含原始动作的 numpy 数组，形状通常为 (batch_size, action_dim)
+    :param stats: 归一化所需的统计信息字典 (包含 'q01' 和 'q99')
+    :param start_idx: 需要归一化的特征在 action 向量中的起始索引
+    :param norm_len: 需要归一化的特征长度，默认为 3 (如 xyz 坐标)
+    :return: 归一化后的动作数组 (返回新数组，不修改原数组)
+    """
+    # 1. 提取并截取对应的 q01 和 q99，保持 float16 精度对齐
+    q01 = np.array(stats["q01"][start_idx : start_idx + norm_len], dtype=np.float16)
+    q99 = np.array(stats["q99"][start_idx : start_idx + norm_len], dtype=np.float16)
+    
+    # 2. 计算分母 denom，防除零保护
+    denom = np.clip(q99 - q01, a_min=1e-5, a_max=None)
+    
+    # 3. 定位切片
+    target_slice = slice(start_idx, start_idx + norm_len)
+    
+    # 4. 复制一份以防污染原始数据矩阵
+    norm_state = raw_state.copy()
+    x = norm_state[:, target_slice]
+    
+    # 5. 执行正向映射计算：y = 2 * (x - q01) / denom - 1
+    y = 2.0 * (x - q01) / denom - 1.0
+    
+    # 6. 核心操作：截断到 [-1.0, 1.0] 区间，防止输入给模型的动作越界
+    norm_state[:, target_slice] = np.clip(y, -1.0, 1.0)
+    
+    return norm_state
+
+def unnormalize_eepose_action(normalized_action, stats, start_idx, norm_len=3):
+    """
+    读取统计信息 JSON 并对动作的特定切片进行反归一化 (Un-normalize)
+    
+    :param normalized_action: 包含归一化动作的 numpy 数组，形状通常为 (batch_size, action_dim)
+    :param stats: 反归一化所需的统计信息，通常是从 JSON 文件中读取的字典
+    :param start_idx: 需要反归一化的特征在 action 向量中的起始索引 (即原代码中的 start)
+    :param norm_len: 需要反归一化的特征长度，默认为 3 (如 xyz 坐标)
+    :return: 反归一化后的动作数组 (返回新数组，不修改原数组)
+    """
+        
+    # 2. 提取并截取对应的 q01 和 q99
+    # 注意：为了和原归一化精度对齐，这里继续保持 float16
+    # import ipdb;ipdb.set_trace()
+    q01 = np.array(stats["q01"][start_idx : start_idx + norm_len], dtype=np.float16)
+    q99 = np.array(stats["q99"][start_idx : start_idx + norm_len], dtype=np.float16)
+    
+    # 3. 计算分母 denom，保持和原来完全相同的裁剪逻辑防除零
+    denom = np.clip(q99 - q01, a_min=1e-5, a_max=None)
+    
+    # 4. 定位切片
+    target_slice = slice(start_idx, start_idx + norm_len)
+    
+    # 5. 执行反向计算：x = (y + 1) / 2 * denom + q01
+    # 复制一份以防污染原始的 normalized_action 矩阵
+    unnorm_action = normalized_action.copy()
+    y = unnorm_action[:, target_slice]
+    unnorm_action[:, target_slice] = (y + 1.0) / 2.0 * denom + q01
+    
+    return unnorm_action
+
+class StarvlaInferenceServer:
+
+    def __init__(self, config_path: str):
+
+        with open(config_path, "r") as f:
+            cfg = yaml.safe_load(f)
+        # import ipdb;ipdb.set_trace()
+        policy_server_cfg = cfg["policy_server"]
+        root_paths = cfg["general"]["root_paths"]
+        self.ckpt_source = policy_server_cfg["ckpt_source"]
+        self.ckpt_path = self._resolve_ckpt_path(
+            ckpt_url=policy_server_cfg["ckpt_path"],
+            root_paths=root_paths,
+        )
+        self.rel_eepose_stats = self.read_rel_eepose_stats()
+        self.host = policy_server_cfg.get("host", "0.0.0.0")
+        self.port = policy_server_cfg.get("port", 5000)
+        self.use_bf16 = policy_server_cfg.get("use_bf16", True)
+        self.unnorm_key = policy_server_cfg.get("unnorm_key", "oxe_bridge")
+        self.state_mode = policy_server_cfg.get("state_mode", "ee_pose7")
+
+        print("Loading StarVLA model...")
+        self.model = self.load_model()
+        print("Model loaded.")
+
+        self.app = Flask(__name__)
+        self.register_routes()
+
+    @staticmethod
+    def _resolve_ckpt_path(ckpt_url: str, root_paths: dict) -> str:
+        parsed = urlparse(ckpt_url)
+        if not parsed.scheme:
+            return ckpt_url
+        root = root_paths.get(parsed.scheme)
+        if not root:
+            raise KeyError(
+                f"cannot find the checkpoint root path in root_paths: {root_paths}"
+            )
+        rel = (parsed.netloc + parsed.path).lstrip("/")
+        return os.path.join(root, rel)
+
+    def read_rel_eepose_stats(self):
+        stats_path = Path(self.ckpt_path).parents[1] / "action_delta_eepose_stats.json"
+        if stats_path is None or not Path(stats_path).exists():
+            return {}
+        with open(stats_path, 'r', encoding='utf-8') as f:
+            stats = json.load(f)
+        return stats
+
+    def load_model(self):
+
+        from starVLA.model.framework.share_tools import read_mode_config, dict_to_namespace
+        from starVLA.model.framework.__init__ import build_framework
+
+        model_config, norm_stats = read_mode_config(self.ckpt_path)
+
+        cfg = dict_to_namespace(model_config)
+        cfg.trainer.pretrained_checkpoint = None
+
+        model = build_framework(cfg=cfg)
+        model.norm_stats = norm_stats
+
+        state_dict = torch.load(self.ckpt_path, map_location="cpu")
+        model.load_state_dict(state_dict, strict=True)
+
+        if self.use_bf16:
+            model = model.to(torch.bfloat16)
+
+        model = model.to("cuda").eval()
+
+        self.norm_stats = norm_stats
+        self.action_norm_stats = norm_stats.get(self.unnorm_key, {}).get("action", None)
+
+        return model
+
+    def parse_observation(self, obs, target_size=(320, 180)):
+
+        left_rgb, right_rgb, wrist_rgb = obs["rgb"]["Left_Camera"], obs["rgb"]["Right_Camera"], obs["rgb"]["Hand_Camera"]
+
+        img_left = Image.fromarray(cv2.resize(left_rgb, target_size))
+        img_right = Image.fromarray(cv2.resize(right_rgb, target_size))
+        img_wrist = Image.fromarray(cv2.resize(wrist_rgb, target_size))
+
+        state_vec = obs["state"] #[:10]
+        # import ipdb;ipdb.set_trace()
+        state_vec = pad_to_dim(np.array(state_vec), 100, axis=-1)
+        return img_left, img_right, img_wrist, state_vec, obs["prompt"]
+
+    def inference(self, observation: dict) -> dict:
+        
+        img_left, img_right, img_wrist, state_vec, prompt = \
+            self.parse_observation(observation)
+        print(f"{state_vec.shape}")
+        vla_input = {
+            "batch_images": [[img_left, img_right, img_wrist]],
+            "instructions": [prompt],
+            "state": [state_vec]
+        }
+        # import ipdb;ipdb.set_trace()
+        with torch.no_grad():
+            output = self.model.predict_action(**vla_input)
+
+        actions = output.get("normalized_actions")
+
+        if isinstance(actions, torch.Tensor):
+            actions = actions.cpu().numpy()
+
+        if actions.ndim == 3:
+            actions = actions[0] # (16, 10)
+        # import ipdb;ipdb.set_trace()
+        # 反归一化特定切片 (假设需要反归一化的部分是 action 向量中的第 0-8 维，即 ee_pose7 + gripper_width) 
+        actions[:, :3] = unnormalize_eepose_action(actions[:, :3], self.rel_eepose_stats, start_idx=0, norm_len=3)
+        # from ipdb import set_trace; set_trace()
+        return {"ee_delta_position_chunks": actions[:, :3].tolist(), 
+                "ee_delta_rot6d_chunks": actions[:, 3:9].tolist(),
+                "gripper_width_chunks": actions[:, 9:10].tolist()}
+
+    def register_routes(self):
+
+        @self.app.route("/policy/inference", methods=["POST"])
+        def policy():
+            try:
+                data = pickle.loads(request.data)
+                result = self.inference(data)
+                body = pickle.dumps(result, protocol=4)
+                return Response(body, mimetype="application/octet-stream")
+            except Exception as e:
+                err_obj = {
+                    "error": str(e),
+                    "traceback": traceback.format_exc(),
+                }
+                body = pickle.dumps(err_obj, protocol=4)
+                return Response(
+                    body,
+                    mimetype="application/octet-stream",
+                    status=500,
+                )
+        @self.app.route("/policy/health", methods=["GET"])
+        def health():
+            if self.model is None:
+                return Response("Failed to load model", mimetype="application/json", status=500)
+            return Response("OK", mimetype="application/json")
+
+    def run(self):
+
+        print("StarVLA policy server running")
+        print(f"Host: {self.host}")
+        print(f"Port: {self.port}")
+
+        self.app.run(
+            host=self.host,
+            port=self.port,
+            threaded=True
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="StarVLA inference server")
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="./benchmark.yaml",
+        help="Path to benchmark.yaml",
+    )
+    args = parser.parse_args()
+
+    config_path = args.config
+    server = StarvlaInferenceServer(config_path)
+    server.run()

starvla_policy.py

@@ -8,8 +8,9 @@ from joysim.annotations.config_class import configclass, field
 from joysim.annotations.stereotype import stereotype
 from joysim.controllers.spawnable_controller import SpawnableController
 from joysim.controllers.visualize_controller import VisualizeController
+from joysim.unisim.robots.models.modular_robot import ModularRobot
 from joysim.utils.namespace import PoseVisualType, SimulatorType
-from joysim.unisim.robots.configs.actuator_configs.grippers import GripperDriveJointConfig
+from joysim.unisim.robots.actuator_configs.grippers import GripperDriveJointConfig
 from joysim.extensions.benchmark.action import RobotAction
 from joysim.extensions.benchmark.benchmark import (
     BenchmarkAction,
@@ -25,6 +26,8 @@ from joysim.utils.pose import Pose
 class StarvlaPolicyConfig(PolicyConfig):
 
     robot_name: str = field(default="None", required=True, comment="The name of the robot")
+    arm_name: str = field(default="main_arm", required=True, comment="The name of the arm module to control")
+    drive_name: str = field(default="robotiq_85_left_knuckle_joint", required=True, comment="The name of the drive module to control")
     gripper_width_mapper_file: str = field(default="", required=True, comment="The file path to the gripper width mapper")
     visualize_action_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the action end effector pose")
     visualize_state_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the state end effector pose")
@@ -64,6 +67,8 @@ class StarvlaPolicy(Policy):
         super().__init__(config)
 
         self.robot_name = config.robot_name
+        self.arm_name = config.arm_name
+        self.drive_name = config.drive_name
         self.sensor_names = config.sensor_names
         self.server_url = config.server_url
         self.prompt = config.prompt
@@ -79,11 +84,15 @@ class StarvlaPolicy(Policy):
         self.current_chunk_id = 0
         self.current_chunk_result = None
         self.run_trunk_size = self.config.run_trunk_size
-        self.drive_joints: dict[str, GripperDriveJointConfig] = SpawnableController.control_robot(self.robot_name, "get_gripper_drive_joints").unwrap()
+        self.robot: ModularRobot = SpawnableController.get_spawnable_data(self.robot_name).unwrap()
+        self.drive_joints: dict[str, GripperDriveJointConfig] = self.robot.get_arm(self.arm_name).get_ee().get_drive_joints()
+        self.robot_drive_name = list(self.drive_joints.keys())[0]
+        
         for joint_name, joint_config in self.drive_joints.items():
             SpawnableController.control_robot(self.robot_name, "set_joint_stiffness", parameters={"joint_names": [joint_name], "stiffness": joint_config.position_control_stiffness}).unwrap()
             SpawnableController.control_robot(self.robot_name, "set_joint_damping", parameters={"joint_names": [joint_name], "damping": joint_config.position_control_damping}).unwrap()
-            SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [joint_name], "effort_limit": 50}).unwrap()
+            SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [joint_name], "effort_limit": 5000}).unwrap()
+            SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [self.robot_drive_name], "effort_limit": 5000}).unwrap()
         self.max_width = float("-inf")
         self.min_width = float("inf")
         for entry in self.gripper_width_mapper:
@@ -113,11 +122,13 @@ class StarvlaPolicy(Policy):
 
     def preprocess_observation(self, benchmark_observation: BenchmarkObservation) -> dict:
         robot_obs = benchmark_observation.get_robot_observations(self.robot_name)["robot_data"]
-        ee_pose_base = robot_obs["ee_pose"]["base_frame"]
+        ee_pose_base = robot_obs["ee_pose"][self.arm_name]["base_frame"]
         ee_position, ee_rot6d = ee_pose_base["position"],ee_pose_base["rot6d"]
-        normalized_gripper_width = self.__map_joint_position_to_normalized_width(robot_obs["gripper_drive_state"]["position"][0])
+        arm_joint_positions = robot_obs["joint_positions"][:7] # 临时多加了一个drive的位置，现在读的最后一个joint值是drive
+        drive_joint_positions = robot_obs["joint_positions"][-1]
+        normalized_gripper_width = self.__map_joint_position_to_normalized_width(drive_joint_positions)
         Log.debug(f"input normalized_gripper_width state: {round(normalized_gripper_width, 2)}")
-        state = np.concatenate([ee_position,ee_rot6d,np.array([normalized_gripper_width])])
+        state = np.concatenate([ee_position,ee_rot6d,np.array([normalized_gripper_width]), [0]*10, np.array(arm_joint_positions)])
         rgb_data = {}
         for sensor_name in self.sensor_names:
             sensor_obs = benchmark_observation.get_sensor_observations(sensor_name)
@@ -137,6 +148,7 @@ class StarvlaPolicy(Policy):
                 data=payload,
                 headers={"Content-Type": "application/octet-stream"}
             )
+            self.test_obs = observation["state"] #TODO
             self._handle_server_error(response)
             result = pickle.loads(response.content)
             max_trunk_size = len(result["ee_delta_position_chunks"])
@@ -176,7 +188,9 @@ class StarvlaPolicy(Policy):
 
     def postprocess_action(self, action: dict) -> BenchmarkAction:
         benchmark_action = BenchmarkAction()
-
+        Log.debug(f"observation: {self.test_obs}")
+        # import ipdb;ipdb.set_trace()
+        
         # get base frame end-effector pose
         delta_ee_pose = Pose(position=action["ee_delta_position_chunks"][self.current_chunk_id], rot6d=action["ee_delta_rot6d_chunks"][self.current_chunk_id])
         curr_state_ee_pose = Pose(position=self.current_ee_position_state, rot6d=self.current_ee_rot6d_state)
@@ -221,7 +235,7 @@ class StarvlaPolicy(Policy):
             VisualizeController.create_pose_visualization(
                 robot_base_world * pose_state_base,
                 name=f"{self.robot_name}/starvla_state_ee",
-                simulator=SimulatorType.ISAACSIM,
+                simulator=SimulatorType.ISAACLAB,
                 pose_type=PoseVisualType.COORDINATE,
                 extra_params={"axis_length": 0.08, "thickness": 0.006},
             ).unwrap()
@@ -229,7 +243,7 @@ class StarvlaPolicy(Policy):
             VisualizeController.create_pose_visualization(
                 robot_base_world * pose_action_base,
                 name=f"{self.robot_name}/starvla_action_ee",
-                simulator=SimulatorType.ISAACSIM,
+                simulator=SimulatorType.ISAACLAB,
                 pose_type=PoseVisualType.COORDINATE,
                 extra_params={"axis_length": 0.1, "thickness": 0.006},
             ).unwrap()
@@ -239,5 +253,5 @@ class StarvlaPolicy(Policy):
             return
         for target_name in self.visualize_bounding_box_targets:
             VisualizeController.visualize_target_bounding_box(
-                target_name, simulator=SimulatorType.ISAACSIM
+                target_name, simulator=SimulatorType.ISAACLAB
             ).unwrap()