243 lines
12 KiB
Python
243 lines
12 KiB
Python
import pickle
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import time
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import json
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import numpy as np
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import requests
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from joysim.annotations.config_class import configclass, field
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from joysim.annotations.stereotype import stereotype
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from joysim.controllers.spawnable_controller import SpawnableController
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from joysim.controllers.visualize_controller import VisualizeController
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from joysim.utils.namespace import PoseVisualType, SimulatorType
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from joysim.unisim.robots.configs.actuator_configs.grippers import GripperDriveJointConfig
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from joysim.extensions.benchmark.action import RobotAction
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from joysim.extensions.benchmark.benchmark import (
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BenchmarkAction,
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BenchmarkObservation,
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ControlMode,
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)
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from joysim.extensions.benchmark.policy import Policy, PolicyConfig
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from joysim.utils.log import Log
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from joysim.utils.pose import Pose
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@configclass
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@stereotype.register_config("starvla")
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class StarvlaPolicyConfig(PolicyConfig):
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robot_name: str = field(default="None", required=True, comment="The name of the robot")
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gripper_width_mapper_file: str = field(default="", required=True, comment="The file path to the gripper width mapper")
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visualize_action_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the action end effector pose")
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visualize_state_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the state end effector pose")
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visualize_bounding_box_targets: list[str] = field(
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default_factory=list,
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required=False,
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comment="Spawnable object names to draw bounding boxes for (empty = off)",
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)
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sensor_names: list[str] = field(
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default=["Hand_Camera", "Left_Camera", "Right_Camera"],
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required=True,
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comment="The names of the sensors"
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)
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server_url: str = field(
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default="http://127.0.0.1:5000/policy",
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required=True,
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comment="StarVLA policy server url"
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)
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prompt: str = field(
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default="pick the object",
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required=True,
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comment="task instruction"
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)
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run_trunk_size: int = field(
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default=16,
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required=True,
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comment="The number of chunks to run in one inference step"
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)
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@stereotype.register_model("starvla")
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class StarvlaPolicy(Policy):
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def __init__(self, config: StarvlaPolicyConfig):
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super().__init__(config)
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self.robot_name = config.robot_name
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self.sensor_names = config.sensor_names
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self.server_url = config.server_url
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self.prompt = config.prompt
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self.gripper_width_mapper = json.load(open(config.gripper_width_mapper_file, "r"))
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self.visualize_action_ee_pose = config.visualize_action_ee_pose
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self.visualize_state_ee_pose = config.visualize_state_ee_pose
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self.visualize_bounding_box_targets = list(config.visualize_bounding_box_targets or [])
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def reset(self) -> None:
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self.current_ee_position_state = None
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self.current_ee_rot6d_state = None
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self.current_gripper_width = None
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self.current_chunk_id = 0
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self.current_chunk_result = None
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self.run_trunk_size = self.config.run_trunk_size
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self.drive_joints: dict[str, GripperDriveJointConfig] = SpawnableController.control_robot(self.robot_name, "get_gripper_drive_joints").unwrap()
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for joint_name, joint_config in self.drive_joints.items():
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SpawnableController.control_robot(self.robot_name, "set_joint_stiffness", parameters={"joint_names": [joint_name], "stiffness": joint_config.position_control_stiffness}).unwrap()
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SpawnableController.control_robot(self.robot_name, "set_joint_damping", parameters={"joint_names": [joint_name], "damping": joint_config.position_control_damping}).unwrap()
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SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [joint_name], "effort_limit": 50}).unwrap()
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self.max_width = float("-inf")
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self.min_width = float("inf")
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for entry in self.gripper_width_mapper:
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self.max_width = max(self.max_width, entry["width"])
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self.min_width = min(self.min_width, entry["width"])
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def warmup(self, benchmark_observation: BenchmarkObservation) -> None:
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Log.info(f"Waiting for StarVLA inference server to be ready...")
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while True:
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try:
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if requests.get(f"{self.server_url}/health", timeout=1.0).status_code == 200:
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break
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except Exception:
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time.sleep(1)
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Log.success(f"StarVLA inference server is ready.")
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def needs_observation(self) -> bool:
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return self.current_chunk_id == 0
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def _handle_server_error(self, response: requests.Response) -> None:
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if response.status_code == 500:
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err_obj = pickle.loads(response.content)
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Log.error(f"StarVLA server error: {err_obj['error']}")
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Log.error(f"Traceback: {err_obj['traceback']}", exit=True)
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elif response.status_code != 200:
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Log.error(f"StarVLA server error with status code <{response.status_code}> : {response.text}", exit=True)
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def preprocess_observation(self, benchmark_observation: BenchmarkObservation) -> dict:
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robot_obs = benchmark_observation.get_robot_observations(self.robot_name)["robot_data"]
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ee_pose_base = robot_obs["ee_pose"]["base_frame"]
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ee_position, ee_rot6d = ee_pose_base["position"],ee_pose_base["rot6d"]
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normalized_gripper_width = self.__map_joint_position_to_normalized_width(robot_obs["gripper_drive_state"]["position"][0])
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Log.debug(f"input normalized_gripper_width state: {round(normalized_gripper_width, 2)}")
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state = np.concatenate([ee_position,ee_rot6d,np.array([normalized_gripper_width])])
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rgb_data = {}
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for sensor_name in self.sensor_names:
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sensor_obs = benchmark_observation.get_sensor_observations(sensor_name)
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rgb_data[sensor_name] = sensor_obs["rgb"].data.cpu().numpy().astype(np.uint8)
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obs = {"state": state,"rgb": rgb_data,"prompt": self.prompt}
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return obs
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def compute_action(self, observation: dict) -> dict:
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if self.current_chunk_result is None:
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self.current_ee_position_state = np.array(observation["state"][:3]).astype(np.float64)
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self.current_ee_rot6d_state = np.array(observation["state"][3:9]).astype(np.float64)
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self.current_gripper_width = np.array([observation["state"][9]])
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payload = pickle.dumps(observation)
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response = requests.post(
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f"{self.server_url}/inference",
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data=payload,
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headers={"Content-Type": "application/octet-stream"}
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)
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self._handle_server_error(response)
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result = pickle.loads(response.content)
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max_trunk_size = len(result["ee_delta_position_chunks"])
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if self.run_trunk_size > max_trunk_size:
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Log.warning(f"Run trunk size {self.run_trunk_size} is greater than the number of chunks {max_trunk_size}. Set run trunk size to {max_trunk_size}.")
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self.run_trunk_size = max_trunk_size
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self.run_trunk_size = max_trunk_size
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self.current_chunk_result = result
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else:
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result = self.current_chunk_result
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return result
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def __map_joint_position_to_normalized_width(self, joint_position: float) -> float:
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if joint_position < 0:
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joint_position = 0
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if joint_position > 0.8:
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joint_position = 0.8
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for entry in self.gripper_width_mapper:
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if round(entry["angel"], 2) == round(joint_position, 2):
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return 1-(entry["width"] - self.min_width) / (self.max_width - self.min_width)
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def __map_gripper_joint_position(self, normalized_gripper_width: float) -> float:
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joint_positions = []
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joint_names = []
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if normalized_gripper_width > 0.5:
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for joint_name, joint_config in self.drive_joints.items():
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joint_positions.append(joint_config.close_position)
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joint_names.append(joint_name)
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else:
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for joint_name, joint_config in self.drive_joints.items():
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joint_positions.append(joint_config.open_position)
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joint_names.append(joint_name)
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return joint_positions, joint_names
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def postprocess_action(self, action: dict) -> BenchmarkAction:
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benchmark_action = BenchmarkAction()
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# get base frame end-effector pose
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delta_ee_pose = Pose(position=action["ee_delta_position_chunks"][self.current_chunk_id], rot6d=action["ee_delta_rot6d_chunks"][self.current_chunk_id])
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curr_state_ee_pose = Pose(position=self.current_ee_position_state, rot6d=self.current_ee_rot6d_state)
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curr_action_ee_pose = curr_state_ee_pose * delta_ee_pose # action2base = state2base * action2state
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curr_action_gripper_width = action["gripper_width_chunks"][self.current_chunk_id]
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gripper_joint_positions, gripper_joint_names = self.__map_gripper_joint_position(curr_action_gripper_width[0])
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Log.debug(f"action_gripper_joint_positions: {gripper_joint_positions}, action_normalized_gripper_width: {round(curr_action_gripper_width[0], 2)}")
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benchmark_action.add_robot_action(
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RobotAction(
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control_mode=ControlMode.POSITION,
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robot_name=self.robot_name,
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joint_names=gripper_joint_names,
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joint_positions=gripper_joint_positions
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)
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)
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benchmark_action.add_robot_action(
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RobotAction(
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control_mode=ControlMode.EE_POSE,
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robot_name=self.robot_name,
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ee_pose=curr_action_ee_pose
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)
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)
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self._visualize_base_frame_ee_poses(curr_state_ee_pose, curr_action_ee_pose)
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self._visualize_bounding_boxes()
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self.current_chunk_id += 1
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if self.current_chunk_id == self.run_trunk_size:
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self.current_chunk_id = 0
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self.current_chunk_result = None
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return benchmark_action
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# ------------------- Visualization -------------------
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def _visualize_base_frame_ee_poses(
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self, pose_state_base: Pose, pose_action_base: Pose
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) -> None:
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if not self.visualize_action_ee_pose and not self.visualize_state_ee_pose:
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return
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robot_base_world = SpawnableController.control_robot(
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self.robot_name, "get_pose"
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).unwrap()
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if self.visualize_state_ee_pose:
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VisualizeController.create_pose_visualization(
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robot_base_world * pose_state_base,
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name=f"{self.robot_name}/starvla_state_ee",
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simulator=SimulatorType.ISAACSIM,
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pose_type=PoseVisualType.COORDINATE,
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extra_params={"axis_length": 0.08, "thickness": 0.006},
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).unwrap()
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if self.visualize_action_ee_pose:
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VisualizeController.create_pose_visualization(
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robot_base_world * pose_action_base,
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name=f"{self.robot_name}/starvla_action_ee",
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simulator=SimulatorType.ISAACSIM,
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pose_type=PoseVisualType.COORDINATE,
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extra_params={"axis_length": 0.1, "thickness": 0.006},
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).unwrap()
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def _visualize_bounding_boxes(self) -> None:
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if not self.visualize_bounding_box_targets:
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return
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for target_name in self.visualize_bounding_box_targets:
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VisualizeController.visualize_target_bounding_box(
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target_name, simulator=SimulatorType.ISAACSIM
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).unwrap() |