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finish benchmark debug

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hufei.hofee
2026-03-19 01:43:02 +08:00
parent cc9815f3b8
commit 1b30c3f96a
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1
.gitignore vendored
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@@ -1 +1,2 @@
.vscode
__pycache__/

28
Readme.md Normal file
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@@ -0,0 +1,28 @@
# StarVLA Benchmark
## 1 Installation
### 1.1 Install Fastsim
Install Fastsim from: [https://git.hofee.cn/hofee/fastsim.git](https://git.hofee.cn/hofee/fastsim.git)
### 1.2 Install StarVLA
Install edited version of StarVLA from `XiongHao Wu`
## 2 Run Benchmark
### 2.1 Run FastSim
```bash
conda activate fastsim
joysim launch_simulation --config ./benchmark.yaml # policy will be auto-scanned.
```
### 2.2 Run StarVLA inference server
```bash
conda activate starvla
python starvla_inference_server.py --config ./benchmark.yaml
```

234
benchmark.yaml
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@@ -2,7 +2,6 @@ general:
scan_project: true
root_paths:
asset: /home/ubuntu/projects/gen_data/data
output: /home/ubuntu/output
checkpoints: /home/ubuntu/data/models
simulation:
@@ -13,160 +12,197 @@ simulation:
livestream: 0
scene:
name: default_scene_name
position: [0, 0, 0]
rotation: [1, 0, 0, 0]
name: kujiale_multispace
base_config:
name: default_base
source: primitive
stereotype: ground_plane
ground_size: [100,100]
stereotype: usd
name: _827313_home_workspace_00
source: local
asset_path: asset://scenes/kujiale_multispace/827313_home/workspace_00.usd
object_cfg_dict:
table:
name: simple_table
position: [0.5, 0, 0.25]
source: primitive
stereotype: rigid
primitive_type: cuboid
primitive_size: [0.5, 1, 0.5]
mass: 1e4
target:
name: target
position: [0.4, 0.0, 0.5]
scale: [0.001, 0.001, 0.001]
axis_y_up: true
asset_path: asset://objects/omni6DPose/ball/omni6DPose_ball_020/Aligned.usd
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:
- 0.0
- -3.79243
- 0.5
quaternion:
- -0.304408012043137
- -0.304408012043137
- 0.638228612805745
- 0.6382286128057448
axis_y_up: true
omni6DPose_plug_001:
name: omni6DPose_plug_001
stereotype: rigid
source: local
asset_path: asset://objects/omni6DPose/plug/omni6DPose_plug_001/Aligned.usd
scale:
- 0.001
- 0.001
- 0.001
position:
- 0.419859
- -4.152430000000001
- 0.510259093
quaternion:
- 0.7070997233636068
- 0.707099723363607
- 0.003159306745230588
- 0.003159306745230588
axis_y_up: true
robot_cfg_dict:
robot:
name: my_robot
Franka:
name: Franka
asset_path: asset://Franka/franka_robotiq_2f85_zedmini.usd
position: [0, 0, 0]
position:
- -0.3779152929316859
- -3.943187336951741
- 0.3130090550954169
rotation:
- 0.9987576855319703
- 0.0
- 0.0
- 0.04983056883903615
stereotype: single_gripper_arm_robot
source: local
init_joint_position:
panda_joint2: -0.1633
panda_joint4: -1.070
panda_joint4: -1.07
panda_joint6: 0.8933
panda_joint7: 0.785
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
world_stage_ignore_substrings: [my_robot]
world_stage_only_paths: [/World]
world_stage_reference_prim_path: /World/Robot/SingleGripperArmRobot/my_robot
sensor_cfg_dict:
front_camera:
name: front_camera
Hand_Camera:
name: Hand_Camera
stereotype: camera
position: [0.8, 0.0, 0.8]
data_types: [rgb, depth, normals]
data_types:
- rgb
- depth
- normals
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
left_camera:
name: left_camera
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
position: [0.6, 0.7, 0.8]
data_types: [rgb, depth, normals]
data_types:
- rgb
- depth
- normals
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
right_camera:
name: right_camera
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
position: [0.6, -0.7, 0.8]
data_types: [rgb, depth, normals]
data_types:
- rgb
- depth
- normals
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
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:
my_data_collect:
benchmark_data_collect:
enable: true
stereotype: data_collect
observer_cfgs:
- stereotype: robot_observer
name: my_robot
observe_joint_positions: true
observe_joint_velocities: true
observe_joint_accelerations: true
observe_joint_position_targets: true
observe_joint_velocity_targets: true
observe_position: true
observe_rotation: true
name: Franka
observe_ee_pose: true
observe_gripper_state: true
observe_gripper_drive_state: true
- stereotype: sensor_observer
name: front_camera
observe_intrinsic_matrix: true
observe_extrinsic_matrix: true
name: Hand_Camera
observe_rgb: true
observe_depth: true
observe_normals: true
- stereotype: sensor_observer
name: left_camera
observe_intrinsic_matrix: true
observe_extrinsic_matrix: true
name: Left_Camera
observe_rgb: true
observe_depth: true
observe_normals: true
- stereotype: sensor_observer
name: right_camera
observe_intrinsic_matrix: true
observe_extrinsic_matrix: true
name: Right_Camera
observe_rgb: true
observe_depth: true
observe_normals: true
- stereotype: task_observer
name: task
- stereotype: object_observer
name: target
observe_position: true
observe_rotation: true
observe_scale: true
my_benchmark:
starvla_benchmark:
enable: true
stereotype: benchmark
data_collector_name: my_data_collect
data_collector_name: benchmark_data_collect
goals:
- name: reach_target
description: Reach the target
stereotype: pose
pose_A_source: ee
pose_A_params:
robot_name: my_robot
pose_B_source: spawnable
pose_B_params:
spawnable_name: target
position_tolerance: 0.005
- 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_B_name: omni6DPose_can_016
policy:
stereotype: starvla
robot_name: my_robot
object_name: target
robot_name: Franka
sensor_names: [Hand_Camera, Left_Camera, Right_Camera]
prompt: pick the cola bottle and place it on the book
policy_server:

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starvla_inference_server.py
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@@ -1,7 +1,9 @@
import yaml
import pickle
import argparse
import os
import traceback
from urllib.parse import urlparse
import numpy as np
import torch
@@ -77,49 +79,17 @@ class StarvlaInferenceServer:
return model
def parse_observation(self, obs):
def parse_observation(self, obs, target_size=(320, 180)):
rgb = obs["rgb"][-1]
state = obs["state"][-1]
joint = obs.get("joint", None)
prompt = obs["prompt"]
left_rgb, right_rgb, wrist_rgb = obs["rgb"]["Left_Camera"], obs["rgb"]["Right_Camera"], obs["rgb"]["Hand_Camera"]
left = rgb[:, :, :3]
right = rgb[:, :, 3:6]
wrist = rgb[:, :, 6:9]
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))
target_size = (320, 180)
state_vec = obs["state"]
left = cv2.resize(left, target_size)
right = cv2.resize(right, target_size)
wrist = cv2.resize(wrist, target_size)
img_left = Image.fromarray(left)
img_right = Image.fromarray(right)
img_wrist = Image.fromarray(wrist)
if self.state_mode == "joint8":
joint_last = joint[-1]
gripper = state[9]
state_vec = np.concatenate(
[joint_last, np.array([gripper])],
axis=0
)
else:
xyz = state[0:3]
rot6d = state[3:9]
gripper = state[9]
state_vec = np.concatenate(
[xyz, rot6d[:3], np.array([gripper])],
axis=0
)
return img_left, img_right, img_wrist, state_vec, prompt
return img_left, img_right, img_wrist, state_vec, obs["prompt"]
def inference(self, observation: dict) -> dict:
@@ -141,18 +111,31 @@ class StarvlaInferenceServer:
actions = actions.cpu().numpy()
if actions.ndim == 3:
actions = actions[0]
return {"action": actions.astype(np.float32)}
actions = actions[0] # (8, 7)
return {"ee_delta_position_chunks": actions[:, :3].tolist(),
"ee_delta_euler_xyz_chunks": actions[:, 3:6].tolist(),
"gripper_chunks": actions[:, 6:7].tolist()}
def register_routes(self):
@self.app.route("/policy", 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,
)
def run(self):
@@ -168,6 +151,15 @@ class StarvlaInferenceServer:
if __name__ == "__main__":
config_path = "./benchmark.yaml"
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()

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starvla_policy.py
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@@ -1,6 +1,9 @@
import pickle
from joysim.annotations.config_class import configclass, field
from joysim.annotations.stereotype import stereotype
from joysim.app import JoySim
from joysim.controllers.motion_plan_controller import MotionPlanController
from joysim.core.scene_manager import SceneManager
from joysim.extensions.benchmark.action import RobotAction
from joysim.extensions.benchmark.benchmark import (
@@ -9,19 +12,21 @@ from joysim.extensions.benchmark.benchmark import (
ControlMode,
)
from joysim.extensions.benchmark.policy import Policy, PolicyConfig
from joysim.utils.log import Log
from joysim.utils.pose import Pose
import numpy as np
import pickle
import requests
@configclass
@stereotype.register_config("starvla")
class StarvlaPolicyConfig(PolicyConfig):
robot_name: str = field(default="my_robot", required=True, comment="The name of the robot")
object_name: str = field(default="target", required=True, comment="The name of the object")
sensor_names: list[str] = field(
default=["Hand_Camera", "Left_Camera", "Right_Camera"],
required=True,
comment="The names of the sensors"
)
server_url: str = field(
default="http://127.0.0.1:5000/policy",
required=True,
@@ -42,70 +47,76 @@ class StarvlaPolicy(Policy):
super().__init__(config)
self.robot_name = config.robot_name
self.object_name = config.object_name
self.sensor_names = config.sensor_names
self.server_url = config.server_url
self.prompt = config.prompt
def reset(self) -> None:
pass
self.current_ee_position_state = None
self.current_ee_euler_xyz_state = None
self.current_gripper_state = None
def warmup(self, benchmark_observation: BenchmarkObservation) -> None:
pass
def needs_observation(self) -> bool:
return True
def _handle_server_error(self, response: requests.Response) -> None:
if response.status_code == 500:
err_obj = pickle.loads(response.content)
Log.error(f"StarVLA server error: {err_obj['error']}")
Log.error(f"Traceback: {err_obj['traceback']}")
exit(0)
elif response.status_code != 200:
Log.error(f"StarVLA server error with status code <{response.status_code}> : {response.text}")
exit(0)
def preprocess_observation(self, benchmark_observation: BenchmarkObservation) -> dict:
robot_obs = benchmark_observation.get_robot_observations(self.robot_name)["robot_data"]
joint_positions = robot_obs["joint_positions"]
robot_position = robot_obs["position"]
robot_quaternion = robot_obs["rotation"]
state = np.concatenate([
robot_position,
robot_quaternion,
np.array([0.0])
])
camera_obs = benchmark_observation.get_sensor_observations()
rgb = camera_obs["rgb"]
obs = {
"state": np.expand_dims(state, axis=0),
"joint": np.expand_dims(joint_positions, axis=0),
"rgb": np.expand_dims(rgb, axis=0),
"prompt": self.prompt
}
ee_pose_base = robot_obs["ee_pose_base"]
ee_position, ee_euler_xyz = ee_pose_base["position"],ee_pose_base["euler_xyz"]
gripper = 1.0 if robot_obs["gripper_state"]["opened"] else 0.0
state = np.concatenate([ee_position,ee_euler_xyz,np.array([gripper])])
self.current_ee_position_state = np.array(ee_position).astype(np.float64)
self.current_ee_euler_xyz_state = np.array(ee_euler_xyz).astype(np.float64)
self.current_gripper_state = np.array([gripper])
rgb_data = {}
for sensor_name in self.sensor_names:
sensor_obs = benchmark_observation.get_sensor_observations(sensor_name)
rgb_data[sensor_name] = sensor_obs["rgb"].data.cpu().numpy().astype(np.uint8)
obs = {"state": state,"rgb": rgb_data,"prompt": self.prompt}
return obs
def compute_action(self, observation: dict) -> dict:
payload = pickle.dumps(observation)
response = requests.post(
self.server_url,
data=payload,
headers={"Content-Type": "application/octet-stream"}
)
if response.status_code != 200:
raise RuntimeError(f"StarVLA server error: {response.text}")
self._handle_server_error(response)
result = pickle.loads(response.content)
return result
def postprocess_action(self, action: dict) -> BenchmarkAction:
benchmark_action = BenchmarkAction()
robot = SceneManager.get_robot(self.robot_name)
joint_names = robot.get_planner().get_plannable_joint_names()
# get base frame end-effector pose # TODO: Make sure add or multiply the current state
ee_position = action["ee_delta_position_chunks"][0] + self.current_ee_position_state
ee_euler_xyz = action["ee_delta_euler_xyz_chunks"][0] + self.current_ee_euler_xyz_state
joint_positions = action["action"][0]
ee_pose = Pose(position=ee_position, euler_xyz=ee_euler_xyz)
ik_result = MotionPlanController.solve_ik(
robot_name=self.robot_name,
base_frame_ee_pose=ee_pose,
).unwrap()
if not ik_result["success"]:
Log.error(f"IK failed: {ik_result['status']}. Ignore this action.")
return benchmark_action
joint_names = ik_result["result"]["plannable_joint_names"]
joint_positions = ik_result["result"]["plannable_joint_positions"][0]
benchmark_action.add_robot_action(
RobotAction(
control_mode=ControlMode.POSITION,
@@ -119,5 +130,5 @@ class StarvlaPolicy(Policy):
if __name__ == "__main__":
js = JoySim("./benchmark.yaml")
js = JoySim("/home/ubuntu/projects/benchmark/benchmark.yaml")
js.start()