159 lines
4.8 KiB
Python
159 lines
4.8 KiB
Python
from pathlib import Path
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import cv_bridge
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import numpy as np
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import rospy
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import scipy.interpolate
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from geometry_msgs.msg import Pose
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from sensor_msgs.msg import Image, CameraInfo
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from robot_utils.spatial import Rotation, Transform
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from robot_utils.ros.conversions import *
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from robot_utils.ros.tf import TransformTree
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from robot_utils.perception import *
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from vgn import vis
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from vgn.detection import VGN, compute_grasps
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def get_policy(name):
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if name == "single-view":
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return SingleViewBaseline()
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elif name == "fixed-trajectory":
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return FixedTrajectoryBaseline()
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else:
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raise ValueError("{} policy does not exist.".format(name))
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class Policy:
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def __init__(self):
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params = rospy.get_param("active_grasp")
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self.frame_id = params["frame_id"]
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# Robot
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self.base_frame_id = params["base_frame_id"]
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self.ee_frame_id = params["ee_frame_id"]
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self.tf = TransformTree()
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self.H_EE_G = Transform.from_list(params["ee_grasp_offset"])
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self.target_pose_pub = rospy.Publisher("/target", Pose, queue_size=10)
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# Camera
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camera_name = params["camera_name"]
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self.cam_frame_id = camera_name + "_optical_frame"
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depth_topic = camera_name + "/depth/image_raw"
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msg = rospy.wait_for_message(camera_name + "/depth/camera_info", CameraInfo)
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self.intrinsic = from_camera_info_msg(msg)
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self.cv_bridge = cv_bridge.CvBridge()
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# TSDF
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self.tsdf = UniformTSDFVolume(0.3, 40)
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# VGN
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params = rospy.get_param("vgn")
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self.vgn = VGN(Path(params["model"]))
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rospy.sleep(1.0)
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self.H_B_T = self.tf.lookup(self.base_frame_id, self.frame_id, rospy.Time.now())
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rospy.Subscriber(depth_topic, Image, self.sensor_cb, queue_size=1)
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vis.draw_workspace(0.3)
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def sensor_cb(self, msg):
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self.last_depth_img = self.cv_bridge.imgmsg_to_cv2(msg).astype(np.float32)
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self.last_extrinsic = self.tf.lookup(
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self.cam_frame_id, self.frame_id, msg.header.stamp, rospy.Duration(0.1)
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)
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def get_tsdf_grid(self):
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map_cloud = self.tsdf.get_map_cloud()
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points = np.asarray(map_cloud.points)
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distances = np.asarray(map_cloud.colors)[:, 0]
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return create_grid_from_map_cloud(points, distances, self.tsdf.voxel_size)
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def plan_best_grasp(self):
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tsdf_grid = self.get_tsdf_grid()
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out = self.vgn.predict(tsdf_grid)
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grasps = compute_grasps(out, voxel_size=self.tsdf.voxel_size)
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vis.draw_tsdf(tsdf_grid, self.tsdf.voxel_size)
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vis.draw_grasps(grasps, 0.05)
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# Ensure that the camera is pointing forward.
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grasp = grasps[0]
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rot = grasp.pose.rotation
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axis = rot.as_matrix()[:, 0]
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if axis[0] < 0:
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grasp.pose.rotation = rot * Rotation.from_euler("z", np.pi)
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# Compute target pose of the EE
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H_T_G = grasp.pose
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H_B_EE = self.H_B_T * H_T_G * self.H_EE_G.inv()
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return H_B_EE
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class SingleViewBaseline(Policy):
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def __init__(sel):
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super().__init__()
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def start(self):
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self.done = False
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def update(self):
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# Integrate image
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self.tsdf.integrate(
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self.last_depth_img,
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self.intrinsic,
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self.last_extrinsic,
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)
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# Visualize reconstruction
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cloud = self.tsdf.get_scene_cloud()
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vis.draw_points(np.asarray(cloud.points))
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# Plan grasp
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self.best_grasp = self.plan_best_grasp()
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self.done = True
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return
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class FixedTrajectoryBaseline(Policy):
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def __init__(self):
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super().__init__()
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self.duration = 4.0
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self.radius = 0.1
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self.m = scipy.interpolate.interp1d([0, self.duration], [np.pi, 3.0 * np.pi])
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def start(self):
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self.tic = rospy.Time.now()
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timeout = rospy.Duration(0.1)
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x0 = self.tf.lookup(self.base_frame_id, self.ee_frame_id, self.tic, timeout)
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self.origin = np.r_[x0.translation[0] + self.radius, x0.translation[1:]]
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self.target = x0
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self.done = False
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def update(self):
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elapsed_time = (rospy.Time.now() - self.tic).to_sec()
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# Integrate image
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self.tsdf.integrate(
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self.last_depth_img,
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self.intrinsic,
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self.last_extrinsic,
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)
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# Visualize current integration
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cloud = self.tsdf.get_scene_cloud()
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vis.draw_points(np.asarray(cloud.points))
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if elapsed_time > self.duration:
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self.best_grasp = self.plan_best_grasp()
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self.done = True
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return
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t = self.m(elapsed_time)
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self.target.translation = (
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self.origin + np.r_[self.radius * np.cos(t), self.radius * np.sin(t), 0.0]
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)
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self.target_pose_pub.publish(to_pose_msg(self.target))
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