def test_urdfpy(tmpdir):
outfn = tmpdir.mkdir('urdf').join('ur5.urdf').strpath
# Load
u = URDF.load('tests/data/ur5/ur5.urdf')
assert isinstance(u, URDF)
for j in u.joints:
assert isinstance(j, Joint)
for l in u.links:
assert isinstance(l, Link)
for t in u.transmissions:
assert isinstance(t, Transmission)
for m in u.materials:
assert isinstance(m, Material)
# Test fk
fk = u.link_fk()
assert isinstance(fk, dict)
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4, 4)
# Test save
u.save(outfn)
nu = URDF.load(outfn)
assert len(u.links) == len(nu.links)
assert len(u.joints) == len(nu.joints)
def register_object(self, body_id, urdf_path, global_scaling=1):
link_id_map = dict()
n = p.getNumJoints(body_id)
link_id_map[p.getBodyInfo(body_id)[0].decode('gb2312')] = -1
for link_id in range(0, n):
link_id_map[p.getJointInfo(body_id, link_id)[
12].decode('gb2312')] = link_id
dir_path = dirname(abspath(urdf_path))
file_name = splitext(basename(urdf_path))[0]
robot = URDF.load(urdf_path)
for link in robot.links:
link_id = link_id_map[link.name]
if len(link.visuals) > 0:
for i, link_visual in enumerate(link.visuals):
mesh_scale = [global_scaling,
global_scaling, global_scaling]\
if link_visual.geometry.mesh.scale is None \
else link_visual.geometry.mesh.scale * global_scaling
self.links.append(
PyBulletRecorder.LinkTracker(
name=file_name + f'_{body_id}_{link.name}_{i}',
body_id=body_id,
link_id=link_id,
link_origin= # If link_id == -1 then is base link,
# PyBullet will return
# inertial_origin @ visual_origin,
# so need to undo that transform
(np.linalg.inv(link.inertial.origin)
if link_id == -1
else np.identity(4)) @
link_visual.origin * global_scaling,
mesh_path=dir_path + '/' +
link_visual.geometry.mesh.filename,
mesh_scale=mesh_scale))
def __init__(self, obstacles) -> None:
self.robot = URDF.load('data/xarm6/xarm6.urdf')
self.spaces = RealVectorSpace(6)
self.robot_cm = CollisionManager()
self.env_cm = CollisionManager()
self.start_config = [0, 0, 0, 0, 0, 0]
self.traj = []
self.count_i = 0
self.curr_q = [0, 0, 0, 0, 0, 0]
cfg = self.get_config([0, 0, 0, 0, 0, 0])
self.init_poses = [0 for i in range(6)]
#print(cfg)
fk = self.robot.collision_trimesh_fk(cfg=cfg)
#fk = self.robot.visual_trimesh_fk(cfg=cfg)
# adding robot to the scene
for i, tm in enumerate(fk):
pose = fk[tm]
name = "link_" + str(i + 1)
self.init_poses.append(pose)
self.robot_cm.add_object(name, tm, pose)
for i, ob in enumerate(obstacles):
self.env_cm.add_object("obstacle_" + str(i), ob)
for i, ob in enumerate(obstacles[:-1]):
self.env_cm.add_object("prediction_" + str(i), ob)
def __init__(self,
path_to_urdf,
active_chain_definition,
use_collision=False,
cfg=None,
visualisation=True,
off_screen=False,
ego_view=True):
self.off_screen = off_screen
self.r_model = URDF.load(path_to_urdf)
self.use_collision = use_collision
self.cfg = cfg
self.viewer = None
self.active_chain_definition = active_chain_definition
self.scene = None
self.rend = None
self.r_node_map = {}
self.visualisation = visualisation
if visualisation:
logger.debug("robot visualisation scene created")
self.create_scene(off_screen=off_screen, ego_view=ego_view)
else:
self.scene = None
self.target_node = None
def _setup_urdf(self):
# Parse the URDF
# TODO: Need to fix this absolute path
self.urdf = URDF.load(osp.join(os.environ['HOME'],
# 'ws_moveit/src/odium/urdf/pr2.urdf'))
'workspaces/odium_ws/src/odium/urdf/pr2.urdf'))
self.link_names = [link.name for link in self.urdf.links]
self.jmap = {}
for j in self.robot.get_joint_names():
if j != 'world_joint':
self.jmap[j] = self.robot.get_joint(j).value()
# self.group.get_end_effector_link()
self.end_link = 'l_gripper_motor_slider_link'
self.cache_fk = {}
return True
def add_object(self, urdf_fn, obj_id, globalScaling=1.0):
# Load urdf file by urdfpy
robot = URDF.load(urdf_fn)
for link_id, link in enumerate(robot.links):
if len(link.visuals) == 0:
continue
link_id = link_id - 1
# Get each links
visual = link.visuals[0]
obj_trimesh = visual.geometry.meshes[0]
# Set mesh color to default (remove texture)
obj_trimesh.visual = trimesh.visual.ColorVisuals()
# Set initial origin (pybullet pose already considered initial origin position, not orientation)
pose = visual.origin
# Scale if it is mesh object (e.g. STL, OBJ file)
mesh = visual.geometry.mesh
if mesh is not None and mesh.scale is not None:
S = np.eye(4, dtype=np.float64)
S[:3, :3] = np.diag(mesh.scale)
pose = pose.dot(S)
# Apply interial origin if applicable
inertial = link.inertial
if inertial is not None and inertial.origin is not None:
pose = np.linalg.inv(inertial.origin).dot(pose)
# Set global scaling
pose = np.diag([globalScaling] * 3 + [1]).dot(pose)
obj_trimesh = obj_trimesh.apply_transform(pose)
obj_name = "{}_{}".format(obj_id, link_id)
self.objects[obj_name] = Link(obj_id, link_id, self.cid)
position, orientation = self.objects[obj_name].get_pose()
# Add object in pyrender
self.renderer.add_object(
obj_trimesh,
obj_name,
position=position, # [-0.015, 0, 0.0235],
orientation=orientation, # [0, 0, 0],
)
def register_object(self, body_id, path):
full_path = abspath(path)
dir_path = dirname(full_path)
file_name = splitext(basename(path))[0]
robot = URDF.load(path)
num_joints = p.getNumJoints(body_id)
assert num_joints + 1 == len(robot.links)
if len(robot.links) == 1:
self.links.append(
PybulletRecorder.LinkTracker(
name=file_name + f'_{body_id}_root',
body_id=body_id,
link_id=-1,
link_origin=robot.links[0].visuals[0].origin,
mesh_path=dir_path + '/' +
robot.links[0].visuals[0].geometry.mesh.filename,
mesh_scale=robot.links[0].visuals[0].geometry.mesh.scale))
else:
self.links.append(
PybulletRecorder.LinkTracker(
name=file_name + f'_{body_id}_root',
body_id=body_id,
link_id=-1,
link_origin=robot.links[0].visuals[0].origin,
mesh_path=dir_path + '/' +
robot.links[0].visuals[0].geometry.mesh.filename,
mesh_scale=robot.links[0].visuals[0].geometry.mesh.scale))
for link_id, link in enumerate(robot.links):
if link_id == 0:
continue
link_id -= 1
if len(link.visuals) > 0:
if p.getLinkState(body_id, link_id) is not None\
and link.visuals[0].geometry.mesh:
# hard code for robotiq
self.links.append(
PybulletRecorder.LinkTracker(
name=file_name + f'_{body_id}_{link.name}',
body_id=body_id,
link_id=link_id,
link_origin=link.visuals[0].origin,
mesh_path=dir_path + '/' +
link.visuals[0].geometry.mesh.filename,
mesh_scale=link.visuals[0].geometry.mesh.scale)
)
def urdf_from_path(path_to_urdf: str) -> URDF:
for file in os.listdir(path_to_urdf):
if is_urdf_file(file):
with open(os.path.join(path_to_urdf, file)) as f:
s = f.read()
break
else:
raise Arcor2UrdfException("Failed to find urdf file.")
s = s.replace("package://", "")
buff = io.BytesIO(s.encode())
buff.name = os.path.join(path_to_urdf, file)
try:
return URDF.load(buff)
except (ValueError, TypeError) as e:
raise Arcor2UrdfException(str(e)) from e
def import_urdf(name, path):
# add '.urdf' extension if it was not added by the user
if not name.endswith(".urdf"):
name += ".urdf"
urdf = URDF.load("{}/{}".format(path, name))
root = urdf.base_link.name
shapes = {}
for link in urdf.links:
if len(link.collisions) == 0:
continue
else:
shapes[link.name] = parse_link(link)
tfs = []
final_shapes = []
for joint in urdf.joints:
if joint.parent == root:
tfs.append(joint.origin)
final_shapes.append(shapes[joint.child])
return Scene(final_shapes, tfs)
def __init__(self, obstacles) -> None:
self.robot = URDF.load('data/ur5/ur5.urdf')
self.spaces = RealVectorSpace(6)
self.robot_cm = CollisionManager()
self.env_cm = CollisionManager()
cfg = self.get_config([0, 0, 0, 0, 0, 0])
self.init_poses = [0 for i in range(6)]
#print(cfg)
fk = self.robot.collision_trimesh_fk(cfg=cfg)
#fk = self.robot.visual_trimesh_fk(cfg=cfg)
# adding robot to the scene
for i, tm in enumerate(fk):
pose = fk[tm]
name = "link_" + str(i + 1)
self.init_poses.append(pose)
self.robot_cm.add_object(name, tm, pose)
table = cylinder(radius=0.7, height=0.02)
table.apply_translation([0, 0, -0.015])
obstacles.append(table)
for i, ob in enumerate(obstacles):
self.env_cm.add_object("obstacle_" + str(i), ob)
def __init__(self, obstacles) -> None:
self.robot = URDF.load('data/two_planar/2dof_planar.urdf')
self.spaces = RealVectorSpace(2)
self.robot_cm = CollisionManager()
self.env_cm = CollisionManager()
self.start_config = [0, 0]
self.traj = []
self.count_i = 0
self.curr_q = [0, 0]
cfg = self.get_config([0, 0])
fk = self.robot.link_fk(cfg=cfg)
self.init_poses = []
for i, tm in enumerate(fk):
pose = fk[tm]
init_pose, link_mesh = self.get_link_mesh(tm)
self.init_poses.append(init_pose)
self.robot_cm.add_object(
tm.name, link_mesh, np.matmul(pose, init_pose))
for i, ob in enumerate(obstacles):
self.env_cm.add_object("obstacle_" + str(i), ob)
self.env_cm.add_object("prediction_" + str(i), ob)
from ikpy.chain import Chain
from ikpy.URDF_utils import get_chain_from_joints
from urdfpy import URDF
import math
import sys
def ang(offset_deg):
abs_deg = 150 + offset_deg
abs_rad = math.pi * abs_deg / 180
return abs_rad
DARWIN_URDF = 'darwin.urdf'
robot = URDF.load(DARWIN_URDF)
joint_names = list(filter(lambda j: j.joint_type != 'fixed', robot.joints))
default_angle = math.pi * 150 / 180
cfg = dict(zip(joint_names, [default_angle] * len(joint_names)))
cfg['neck_joint'] = ang(-60)
cfg['l_shoulder_joint'] = ang(-60)
cfg['l_biceps_joint'] = ang(60)
cfg['l_elbow_joint'] = ang(-120) # limit: 0-150
cfg['r_shoulder_joint'] = ang(-60)
cfg['r_biceps_joint'] = ang(-60)
cfg['r_elbow_joint'] = ang(120) # limit:150-300
cfg['l_hip_joint'] = ang(30)
cfg['l_thigh_joint'] = ang(-60)
cfg['l_knee_joint'] = ang(30)
cfg['l_ankle_joint'] = ang(30)
cfg['l_foot_joint'] = ang(-30)
cfg['r_hip_joint'] = ang(-30)
'joint_head_tilt': head_tilt_rad
}
return configuration
# #######################################################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Python based URDF visualization')
parser.add_argument("--motion", help="Turn motor on", action="store_true")
parser.add_argument("--fake", help="Show fake robot", action="store_true")
args = parser.parse_args()
urdf_file = os.path.join(os.environ['HELLO_FLEET_PATH'], os.environ['HELLO_FLEET_ID'], 'exported_urdf/stretch.urdf')
controller_parameters_filename = os.path.join(os.environ['HELLO_FLEET_PATH'], os.environ['HELLO_FLEET_ID'], 'exported_urdf/controller_calibration_head.yaml')
robot_model = URDF.load(urdf_file)
if args.motion:
cfg_trajectory = {
'joint_left_wheel': [0.0, math.pi],
'joint_right_wheel': [0.0, math.pi],
'joint_lift': [0.9, 0.7],
'joint_arm_l0': [0.0, 0.1],
'joint_arm_l1': [0.0, 0.1],
'joint_arm_l2': [0.0, 0.1],
'joint_arm_l3': [0.0, 0.1],
'joint_wrist_yaw': [math.pi, 0.0],
'joint_gripper_finger_left': [0.0, 0.25],
'joint_gripper_finger_right': [0.0, 0.25],
'joint_head_pan': [0.0, -(math.pi / 2.0)],
'joint_head_tilt': [0.5, -0.5]
target_output = torch.from_numpy(right_hand_data.iloc[:WINDOW_SIZE].values)
target_output = target_output.repeat(BATCH_SIZE, 1, 1)
target_output.requires_grad = False
true_cfg = joint_data_to_urdf_joint_state(data=target_output, data_keys=adl_right_hand_joint_names, hand_keys=RightHand)
# Instantiation of loss module and configuration ________________________________________________________________
# As some of the links in our URDF do not have an equivalent on the dataset, they have to be removed
urdf_relevant_links = ['palm_base', 'palm_link', 'thumb_knuckle_link', 'thumb_proximal_link',
'thumb_middle_link', 'index_knuckle_link', 'index_proximal_link',
'index_middle_link', 'middle_knuckle_link', 'middle_proximal_link',
'middle_middle_link', 'ring_knuckle_link', 'ring_proximal_link',
'ring_middle_link', 'little_knuckle_link', 'little_proximal_link',
'little_middle_link']
# An instance of the hand URDF is required
urdf = URDF.load('robots/right_hand_relative.urdf')
# Instantiation of the loss module, which have two types of loss functions:
# 1
fk_loss = CartesianSpaceLoss(urdf, loss_type='frobenius', relevant_links=urdf_relevant_links)
# 2
# fk_loss = CartesianSpaceLoss(urdf, loss_type='rot_only', relevant_links=urdf_relevant_links)
start = timeit.default_timer()
# Loss forward pass example:
loss = fk_loss(pred_cfg, true_cfg)
stop = timeit.default_timer()
print('\nLoss forward enlapsed time (CPU) for batch size %d and window size %s: %.5fs' % (BATCH_SIZE, WINDOW_SIZE, stop - start))
# Test that the gradients are being computed and propagated appropriately _____________________________________
loss.backward()
def test_urdfpy(tmpdir):
outfn = tmpdir.mkdir('urdf').join('ur5.urdf').strpath
# Load
u = URDF.load('tests/data/ur5/ur5.urdf')
assert isinstance(u, URDF)
for j in u.joints:
assert isinstance(j, Joint)
for l in u.links:
assert isinstance(l, Link)
for t in u.transmissions:
assert isinstance(t, Transmission)
for m in u.materials:
assert isinstance(m, Material)
# Test fk
fk = u.link_fk()
assert isinstance(fk, dict)
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4,4)
fk = u.link_fk({'shoulder_pan_joint': 2.0})
assert isinstance(fk, dict)
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4,4)
fk = u.link_fk(np.zeros(6))
assert isinstance(fk, dict)
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4,4)
fk = u.link_fk(np.zeros(6), link='upper_arm_link')
assert isinstance(fk, np.ndarray)
assert fk.shape == (4,4)
fk = u.link_fk(links=['shoulder_link', 'upper_arm_link'])
assert isinstance(fk, dict)
assert len(fk) == 2
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4,4)
fk = u.link_fk(links=list(u.links)[:2])
assert isinstance(fk, dict)
assert len(fk) == 2
for l in fk:
assert isinstance(l, Link)
assert isinstance(fk[l], np.ndarray)
assert fk[l].shape == (4,4)
cfg={j.name: 0.5 for j in u.actuated_joints}
for _ in range(1000):
fk = u.collision_trimesh_fk(cfg=cfg)
for key in fk:
assert isinstance(fk[key], np.ndarray)
assert fk[key].shape == (4,4)
cfg = {j.name: np.random.uniform(size=1000) for j in u.actuated_joints}
fk = u.link_fk_batch(cfgs=cfg)
for key in fk:
assert isinstance(fk[key], np.ndarray)
assert fk[key].shape == (1000,4,4)
cfg={j.name: 0.5 for j in u.actuated_joints}
for _ in range(1000):
fk = u.collision_trimesh_fk(cfg=cfg)
for key in fk:
assert isinstance(key, trimesh.Trimesh)
assert fk[key].shape == (4,4)
cfg = {j.name: np.random.uniform(size=1000) for j in u.actuated_joints}
fk = u.collision_trimesh_fk_batch(cfgs=cfg)
for key in fk:
assert isinstance(key, trimesh.Trimesh)
assert fk[key].shape == (1000,4,4)
# Test save
u.save(outfn)
nu = URDF.load(outfn)
assert len(u.links) == len(nu.links)
assert len(u.joints) == len(nu.joints)
# Test join
with pytest.raises(ValueError):
x = u.join(u, link=u.link_map['tool0'])
x = u.join(u, link=u.link_map['tool0'], name='copy', prefix='prefix')
assert isinstance(x, URDF)
assert x.name == 'copy'
assert len(x.joints) == 2 * len(u.joints) + 1
assert len(x.links) == 2 * len(u.links)
# Test scale
x = u.copy(scale=3)
assert isinstance(x, URDF)
x = x.copy(scale=[1,1,3])
assert isinstance(x, URDF)
from urdfpy import URDF
robot = URDF.load(
"/home/frank/Documents/can_motor/two_arm_neck/two_arm_neck.urdf")
for link in robot.links:
print(link.name)
for joint in robot.joints:
print('{} connects {} to {}'.format(joint.name, joint.parent, joint.child))
for joint in robot.actuated_joints:
print(joint.name)
#fk = robot.link_fk(cfg={'shoulder_pan_joint' : 1.0})
#print(fk[robot.links[1]])
def calibrate_robot(
robot_joints: list[Joint],
robot_pose: Pose,
camera_pose: Pose,
camera_parameters: CameraParameters,
robot: URDF,
depth_image: Image,
draw_results: bool = False,
) -> Pose:
logger.info("Creating robot model...")
fk = robot.visual_trimesh_fk(cfg={joint.name: joint.value for joint in robot_joints})
robot_tr_matrix = robot_pose.as_tr_matrix()
res_mesh = o3d.geometry.TriangleMesh()
for tm in fk:
pose = fk[tm]
mesh = o3d.geometry.TriangleMesh(
vertices=o3d.utility.Vector3dVector(tm.vertices), triangles=o3d.utility.Vector3iVector(tm.faces)
)
mesh.transform(np.dot(robot_tr_matrix, pose))
mesh.compute_vertex_normals()
res_mesh += mesh
mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1)
sim_pcd = res_mesh.sample_points_uniformly(int(1e5))
camera_tr_matrix = camera_pose.as_tr_matrix()
camera_matrix = camera_parameters.as_camera_matrix()
depth = depth_image_to_np(depth_image)
wh = (depth_image.width, depth_image.height)
dist = np.array(camera_parameters.dist_coefs)
newcameramtx, _ = cv2.getOptimalNewCameraMatrix(camera_matrix, dist, wh, 1, wh)
depth = cv2.undistort(depth, camera_matrix, dist, None, newcameramtx)
real_pcd = o3d.geometry.PointCloud.create_from_depth_image(
o3d.geometry.Image(depth),
o3d.camera.PinholeCameraIntrinsic(
depth_image.width,
depth_image.height,
camera_parameters.fx,
camera_parameters.fy,
camera_parameters.cx,
camera_parameters.cy,
),
)
real_pcd.transform(camera_tr_matrix)
bb = sim_pcd.get_axis_aligned_bounding_box()
real_pcd = real_pcd.crop(bb.scale(1.25, bb.get_center()))
# logger.info("Outlier removal...")
# real_pcd = real_pcd.remove_radius_outlier(nb_points=50, radius=0.01)[0]
sim_pcd = sim_pcd.select_by_index(sim_pcd.hidden_point_removal(np.array(list(camera_pose.position)), 500)[1])
print("Estimating normals...")
real_pcd.estimate_normals()
if draw_results:
o3d.visualization.draw_geometries([sim_pcd, real_pcd, mesh_frame])
threshold = 1.0
trans_init = np.identity(4)
"""
evaluation = o3d.pipelines.registration.evaluate_registration(
sim_pcd,
real_pcd,
threshold,
trans_init,
)
logger.info(evaluation)
"""
logger.info("Applying point-to-plane robust ICP...")
loss = o3d.pipelines.registration.TukeyLoss(k=0.25)
p2l = o3d.pipelines.registration.TransformationEstimationPointToPlane(loss)
reg_p2p = o3d.pipelines.registration.registration_icp(
sim_pcd,
real_pcd,
threshold,
trans_init,
p2l,
o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=100),
)
logger.info(reg_p2p)
logger.debug(reg_p2p.transformation)
# TODO somehow check if calibration is ok
if draw_results:
draw_registration_result(sim_pcd, real_pcd, trans_init, reg_p2p.transformation)
robot_tr_matrix = np.dot(reg_p2p.transformation, robot_tr_matrix)
pose = Pose.from_tr_matrix(robot_tr_matrix)
logger.info("Done")
return pose
from urdfpy import URDF
robot = URDF.load('olympian.urdf')
for link in robot.links:
print(link.name)
print(robot.base_link.name)
def setup_animation_scene(real_traj,
pred_traj,
urdf_path,
hand_offset,
loop_time,
real_color,
pred_color_cmap,
background_color,
reverse=False):
pred_trajectories = pred_traj if isinstance(pred_traj,
list) else [pred_traj]
real_hand = URDF.load(urdf_path)
pred_hands = []
for i, traj in enumerate(pred_trajectories):
pred_hands.append(URDF.load(urdf_path))
pred_origin = pred_hands[i].joint_map[
'palm_joint_abduction'].origin[:3, 3]
pred_hands[i].joint_map[
'palm_joint_abduction'].origin[:3, 3] = pred_origin + np.array(
[hand_offset * (i + 1), 0.0, 0.0])
traj_len = None # Length of the trajectory in steps
# If it is specified, parse it and extract the trajectory length.
for pred_traj in pred_trajectories:
if isinstance(real_traj, dict) and isinstance(pred_traj, dict):
if len(real_traj) > 0:
for joint_name in real_traj:
if len(real_traj[joint_name]) != len(
pred_traj[joint_name]):
raise ValueError(
'Real and pred trajectories [%d] must be same length'
% joint_name)
elif traj_len is None:
traj_len = len(real_traj[joint_name])
elif traj_len != len(real_traj[joint_name]):
raise ValueError(
'All joint trajectories must be same length')
else:
raise TypeError(
'Invalid loss_type for trajectories real[%s], pred[%s]' %
(type(real_traj), type(pred_traj)))
# Create an array of times that loops from 0 to 1 and back to 0
fps = 30.0
# Create the new interpolated trajectory
if reverse:
n_steps = int(loop_time * 2 * fps)
times = np.linspace(0.0, 1.0, n_steps)
times = np.hstack((times, np.flip(times)))
# Create bin edges in the range [0, 1] for each trajectory step
bins = np.arange(traj_len) / (float(traj_len) - 1.0)
# Compute alphas for each time
right_inds = np.digitize(times, bins, right=True)
right_inds[right_inds == 0] = 1
alphas = ((bins[right_inds] - times) /
(bins[right_inds] - bins[right_inds - 1]))
for joint_name in real_traj:
real_traj[joint_name] = (
alphas * real_traj[joint_name][right_inds - 1] +
(1.0 - alphas) * real_traj[joint_name][right_inds])
# Compute all
for pred_traj in pred_trajectories:
pred_traj[joint_name] = (
alphas * pred_traj[joint_name][right_inds - 1] +
(1.0 - alphas) * pred_traj[joint_name][right_inds])
else:
n_steps = int(loop_time * fps)
times = np.linspace(0.0, 1.0, n_steps)
# Create the scene
fk_real = real_hand.visual_trimesh_fk()
fk_preds = [pred_hand.visual_trimesh_fk() for pred_hand in pred_hands]
node_map = {}
scene = pyrender.Scene(bg_color=background_color)
# Spawn ground truth hand
for tm_real in fk_real:
# Little hack to overcome the urdfpy bug of ignoring URDF materials for .stl meshes
tm_real._visual.face_colors = tm_real._visual.face_colors * 0 + real_color
# Real hand nodes
pose = fk_real[tm_real]
real_mesh = pyrender.Mesh.from_trimesh(tm_real, smooth=False)
node = scene.add(real_mesh, pose=pose)
node_map[tm_real] = node
# Spawn prediction hands
pred_color = []
for color_code in np.linspace(0, 1, len(pred_trajectories)):
pred_color.append(pred_color_cmap(color_code, bytes=True))
for i, fk_pred in enumerate(fk_preds):
for tm_pred in fk_pred:
# Little hack to overcome the urdfpy bug of ignoring URDF materials for .stl meshes
tm_pred._visual.face_colors = tm_pred._visual.face_colors * 0 + pred_color[
i]
# Pred hand nodes
pose = fk_pred[tm_pred]
pred_mesh = pyrender.Mesh.from_trimesh(tm_pred, smooth=False)
node = scene.add(pred_mesh, pose=pose)
node_map[tm_pred] = node
# Get base pose to focus on
origin = real_hand.link_fk(
links=[real_hand.base_link])[real_hand.base_link]
return scene, origin, node_map, real_hand, pred_hands, pred_trajectories, times, fps
def yumi2graph(urdf_file, cfg):
# load URDF
robot = URDF.load(urdf_file)
# parse joint params
joints = {}
for joint in robot.joints:
# joint atributes
joints[joint.name] = {
'type': joint.joint_type,
'axis': joint.axis,
'parent': joint.parent,
'child': joint.child,
'origin': matrix_to_xyz_rpy(joint.origin),
'lower': joint.limit.lower if joint.limit else -float('Inf'),
'upper': joint.limit.upper if joint.limit else float('Inf')
}
# debug msg
# for name, attr in joints.items():
# print(name, attr)
# skeleton type & topology type
skeleton_type = 0
topology_type = 0
# collect edge index & edge feature
joints_name = cfg['joints_name']
joints_index = {name: i for i, name in enumerate(joints_name)}
edge_index = []
edge_attr = []
for edge in cfg['edges']:
parent, child = edge
# add edge index
edge_index.append(
torch.LongTensor([joints_index[parent], joints_index[child]]))
# add edge attr
edge_attr.append(torch.Tensor(joints[child]['origin']))
edge_index = torch.stack(edge_index, dim=0)
edge_index = edge_index.permute(1, 0)
edge_attr = torch.stack(edge_attr, dim=0)
# print(edge_index, edge_attr, edge_index.shape, edge_attr.shape)
# number of nodes
num_nodes = len(joints_name)
# end effector mask
ee_mask = torch.zeros(len(joints_name), 1).bool()
for ee in cfg['end_effectors']:
ee_mask[joints_index[ee]] = True
# shoulder mask
sh_mask = torch.zeros(len(joints_name), 1).bool()
for sh in cfg['shoulders']:
sh_mask[joints_index[sh]] = True
# elbow mask
el_mask = torch.zeros(len(joints_name), 1).bool()
for el in cfg['elbows']:
el_mask[joints_index[el]] = True
# node parent
parent = -torch.ones(len(joints_name)).long()
for edge in edge_index.permute(1, 0):
parent[edge[1]] = edge[0]
# node offset
offset = torch.stack(
[torch.Tensor(joints[joint]['origin']) for joint in joints_name],
dim=0)
# change root offset to store init pose
init_pose = {}
fk = robot.link_fk()
for link, matrix in fk.items():
init_pose[link.name] = matrix_to_xyz_rpy(matrix)
origin = torch.zeros(6)
for root in cfg['root_name']:
offset[joints_index[root]] = torch.Tensor(
init_pose[joints[root]['child']])
origin[:3] += offset[joints_index[root]][:3]
origin /= 2
# move relative to origin
for root in cfg['root_name']:
offset[joints_index[root]] -= origin
# print(offset, offset.shape)
# dist to root
root_dist = torch.zeros(len(joints_name), 1)
for node_idx in range(len(joints_name)):
dist = 0
current_idx = node_idx
while current_idx != -1:
origin = offset[current_idx]
offsets_mod = math.sqrt(origin[0]**2 + origin[1]**2 + origin[2]**2)
dist += offsets_mod
current_idx = parent[current_idx]
root_dist[node_idx] = dist
# print(root_dist, root_dist.shape)
# dist to shoulder
shoulder_dist = torch.zeros(len(joints_name), 1)
for node_idx in range(len(joints_name)):
dist = 0
current_idx = node_idx
while current_idx != -1 and joints_name[current_idx] not in cfg[
'shoulders']:
origin = offset[current_idx]
offsets_mod = math.sqrt(origin[0]**2 + origin[1]**2 + origin[2]**2)
dist += offsets_mod
current_idx = parent[current_idx]
shoulder_dist[node_idx] = dist
# print(shoulder_dist, shoulder_dist.shape)
# dist to elbow
elbow_dist = torch.zeros(len(joints_name), 1)
for node_idx in range(len(joints_name)):
dist = 0
current_idx = node_idx
while current_idx != -1 and joints_name[current_idx] not in cfg[
'elbows']:
origin = offset[current_idx]
offsets_mod = math.sqrt(origin[0]**2 + origin[1]**2 + origin[2]**2)
dist += offsets_mod
current_idx = parent[current_idx]
elbow_dist[node_idx] = dist
# print(elbow_dist, elbow_dist.shape)
# rotation axis
axis = [torch.Tensor(joints[joint]['axis']) for joint in joints_name]
axis = torch.stack(axis, dim=0)
# joint limit
lower = [torch.Tensor([joints[joint]['lower']]) for joint in joints_name]
lower = torch.stack(lower, dim=0)
upper = [torch.Tensor([joints[joint]['upper']]) for joint in joints_name]
upper = torch.stack(upper, dim=0)
# print(lower.shape, upper.shape)
# skeleton
data = Data(x=torch.zeros(num_nodes, 1),
edge_index=edge_index,
edge_attr=edge_attr,
skeleton_type=skeleton_type,
topology_type=topology_type,
ee_mask=ee_mask,
sh_mask=sh_mask,
el_mask=el_mask,
root_dist=root_dist,
shoulder_dist=shoulder_dist,
elbow_dist=elbow_dist,
num_nodes=num_nodes,
parent=parent,
offset=offset,
axis=axis,
lower=lower,
upper=upper)
# test forward kinematics
# print(joints_name)
# result = robot.link_fk(cfg={joint:0.0 for joint in joints_name})
# for link, matrix in result.items():
# print(link.name, matrix)
# import os, sys, inspect
# currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# parentdir = os.path.dirname(currentdir)
# sys.path.insert(0,parentdir)
# from models.kinematics import ForwardKinematicsURDF
# fk = ForwardKinematicsURDF()
# pos = fk(data.x, data.parent, data.offset, 1)
# # visualize
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.set_xlabel('X')
# ax.set_ylabel('Y')
# ax.set_zlabel('Z')
# # plot 3D lines
# for edge in edge_index.permute(1, 0):
# line_x = [pos[edge[0]][0], pos[edge[1]][0]]
# line_y = [pos[edge[0]][1], pos[edge[1]][1]]
# line_z = [pos[edge[0]][2], pos[edge[1]][2]]
# plt.plot(line_x, line_y, line_z, 'royalblue', marker='o')
# # plt.show()
# plt.savefig('foo.png')
return data
def loadRobotURDF(fn):
return URDF.load(fn)
