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import math
# import torch
# from ..utils import Timer
import numpy as np
# import torch.nn.functional as F
import os
import argparse
from xml.etree.ElementTree import ElementTree
import trimesh
import torch
import torch.nn as nn
# import List
# class link; joint; body
###
## calculate transformation to the frame ##
## the joint idx ##
##### th_cuda_idx #####
# name to the main axis? #
# def get_body_name_to_main_axis()
# another one is just getting the joint offset positions?
# and after the revolute transformation # # all revolute joint points ####
def get_body_name_to_main_axis():
# negative y; positive x #
body_name_to_main_axis = {
"body2": -2, "body6": 1, "body10": 1, "body14": 1, "body17": 1
}
return body_name_to_main_axis ## get the body name to main axis ##
## insert one
def plane_rotation_matrix_from_angle_xz(angle):
## angle of
sin_ = torch.sin(angle)
cos_ = torch.cos(angle)
zero_padding = torch.zeros_like(cos_)
one_padding = torch.ones_like(cos_)
col_a = torch.stack(
[cos_, zero_padding, sin_], dim=0
)
col_b = torch.stack(
[zero_padding, one_padding, zero_padding], dim=0
)
col_c = torch.stack(
[-1. * sin_, zero_padding, cos_], dim=0
)
rot_mtx = torch.stack(
[col_a, col_b, col_c], dim=-1
)
# col_a = torch.stack(
# [cos_, sin_], dim=0 ### col of the rotation matrix
# )
# col_b = torch.stack(
# [-1. * sin_, cos_], dim=0 ## cols of the rotation matrix
# )
# rot_mtx = torch.stack(
# [col_a, col_b], dim=-1 ### rotation matrix
# )
return rot_mtx
def plane_rotation_matrix_from_angle(angle):
## angle of
sin_ = torch.sin(angle)
cos_ = torch.cos(angle)
col_a = torch.stack(
[cos_, sin_], dim=0 ### col of the rotation matrix
)
col_b = torch.stack(
[-1. * sin_, cos_], dim=0 ## cols of the rotation matrix
)
rot_mtx = torch.stack(
[col_a, col_b], dim=-1 ### rotation matrix
)
return rot_mtx
def rotation_matrix_from_axis_angle(axis, angle): # rotation_matrix_from_axis_angle ->
# sin_ = np.sin(angle) # ti.math.sin(angle)
# cos_ = np.cos(angle) # ti.math.cos(angle)
sin_ = torch.sin(angle) # ti.math.sin(angle)
cos_ = torch.cos(angle) # ti.math.cos(angle)
u_x, u_y, u_z = axis[0], axis[1], axis[2]
u_xx = u_x * u_x
u_yy = u_y * u_y
u_zz = u_z * u_z
u_xy = u_x * u_y
u_xz = u_x * u_z
u_yz = u_y * u_z ##
# rot_mtx = np.stack(
# [np.array([cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], dtype=np.float32),
# np.array([u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], dtype=np.float32),
# np.array([u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], dtype=np.float32)
# ], axis=-1 ### np stack
# ) ## a single
# rot_mtx = torch.stack(
# [
# torch.tensor([cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], dtype=torch.float32),
# torch.tensor([u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], dtype=torch.float32),
# torch.tensor([u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], dtype=torch.float32)
# ], dim=-1 ## stack those torch tensors ##
# )
row_a = torch.stack(
[cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], dim=0
)
# print(f"row_a: {row_a.size()}")
row_b = torch.stack(
[u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], dim=0
)
# print(f"row_b: {row_b.size()}")
row_c = torch.stack(
[u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], dim=0
)
# print(f"row_c: {row_c.size()}")
### rot_mtx for the rot_mtx ###
rot_mtx = torch.stack(
[row_a, row_b, row_c], dim=-1 ### rot_matrix of he matrix ##
)
# rot_mtx = torch.stack(
# [
# torch.tensor([cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], dtype=torch.float32),
# torch.tensor([u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], dtype=torch.float32),
# torch.tensor([u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], dtype=torch.float32)
# ], dim=-1 ## stack those torch tensors ##
# )
# rot_mtx_numpy = rot_mtx.to_numpy()
# rot_mtx_at_rot_mtx = rot_mtx @ rot_mtx.transpose()
# print(rot_mtx_at_rot_mtx)
return rot_mtx
## joint name = "joint3" ##
# <joint name="joint3" type="revolute" axis="0.000000 0.000000 -1.000000" pos="4.689700 -4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e7"/>
class Joint:
def __init__(self, name, joint_type, axis, pos, quat, frame, damping, args) -> None:
self.name = name
self.type = joint_type
self.axis = axis
self.pos = pos
self.quat = quat
self.frame = frame
self.damping = damping
self.args = args
#### TODO: the dimension of the state vector ? ####
# self.state = 0. ## parameter
self.state = nn.Parameter(
torch.zeros((1,), dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
)
# self.rot_mtx = np.eye(3, dtypes=np.float32)
# self.trans_vec = np.zeros((3,), dtype=np.float32) ## rot m
self.rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True)
self.trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True)
# self.rot_mtx = np.eye(3, dtype=np.float32)
# self.trans_vec = np.zeros((3,), dtype=np.float32)
self.axis_rot_mtx = torch.tensor(
[
[1, 0, 0], [0, -1, 0], [0, 0, -1]
], dtype=torch.float32
).cuda(self.args.th_cuda_idx)
self.joint_idx = -1
self.transformed_joint_pts = self.pos.clone()
def print_grads(self, ):
print(f"rot_mtx: {self.rot_mtx.grad}")
print(f"trans_vec: {self.trans_vec.grad}")
def clear_grads(self,):
if self.rot_mtx.grad is not None:
self.rot_mtx.grad.data = self.rot_mtx.grad.data * 0.
if self.trans_vec.grad is not None:
self.trans_vec.grad.data = self.trans_vec.grad.data * 0.
def compute_transformation(self,):
# use the state to transform them # # transform # ## transform the state ##
# use the state to transform them # # transform them for the state #
if self.type == "revolute":
# print(f"computing transformation matrices with axis: {self.axis}, state: {self.state}")
# rotation matrix from the axis angle #
rot_mtx = rotation_matrix_from_axis_angle(self.axis, self.state)
# rot_mtx(p - p_v) + p_v -> rot_mtx p - rot_mtx p_v + p_v
# trans_vec = self.pos - np.matmul(rot_mtx, self.pos.reshape(3, 1)).reshape(3)
# self.rot_mtx = np.copy(rot_mtx)
# self.trans_vec = np.copy(trans_vec)
trans_vec = self.pos - torch.matmul(rot_mtx, self.pos.view(3, 1)).view(3).contiguous()
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec
else:
### TODO: implement transformations for joints in other types ###
pass
def set_state(self, name_to_state):
if self.name in name_to_state:
# self.state = name_to_state["name"]
self.state = name_to_state[self.name] ##
def set_state_via_vec(self, state_vec): ### transform points via the state vectors here ###
if self.joint_idx >= 0:
self.state = state_vec[self.joint_idx] ## give the parameter to the parameters ##
def set_joint_idx(self, joint_name_to_idx):
if self.name in joint_name_to_idx:
self.joint_idx = joint_name_to_idx[self.name]
def set_args(self, args):
self.args = args
def compute_transformation_via_state_vals(self, state_vals):
if self.joint_idx >= 0:
cur_joint_state = state_vals[self.joint_idx]
else:
cur_joint_state = self.state
# use the state to transform them # # transform # ## transform the state ##
# use the state to transform them # # transform them for the state #
if self.type == "revolute":
# print(f"computing transformation matrices with axis: {self.axis}, state: {self.state}")
# rotation matrix from the axis angle #
rot_mtx = rotation_matrix_from_axis_angle(self.axis, cur_joint_state)
# rot_mtx(p - p_v) + p_v -> rot_mtx p - rot_mtx p_v + p_v
# trans_vec = self.pos - np.matmul(rot_mtx, self.pos.reshape(3, 1)).reshape(3)
# self.rot_mtx = np.copy(rot_mtx)
# self.trans_vec = np.copy(trans_vec)
trans_vec = self.pos - torch.matmul(rot_mtx, self.pos.view(3, 1)).view(3).contiguous()
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec
elif self.type == "free2d":
cur_joint_state = state_vals # still only for the current scene #
# cur_joint_state
cur_joint_rot_val = state_vals[2]
### rot_mtx ### ### rot_mtx ###
rot_mtx = plane_rotation_matrix_from_angle_xz(cur_joint_rot_val)
# rot_mtx = plane_rotation_matrix_from_angle(cur_joint_rot_val) ### 2 x 2 rot matrix #
# cur joint rot val #
# rot mtx of the rotation
# xy_val =
# axis_rot_mtx
# R_axis^T ( R R_axis (p) + trans (with the y-axis padded) )
cur_trans_vec = torch.stack(
[state_vals[0], torch.zeros_like(state_vals[0]), state_vals[1]], dim=0
)
# cur_trans_vec #
rot_mtx = torch.matmul(self.axis_rot_mtx.transpose(1, 0), torch.matmul(rot_mtx, self.axis_rot_mtx))
trans_vec = torch.matmul(self.axis_rot_mtx.transpose(1, 0), cur_trans_vec.unsqueeze(-1).contiguous()).squeeze(-1).contiguous() + self.pos
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec ## rot_mtx and trans_vec #
else:
### TODO: implement transformations for joints in other types ###
pass
return self.rot_mtx, self.trans_vec
def transform_joints_via_parent_rot_trans_infos(self, parent_rot_mtx, parent_trans_vec):
#
# if self.type == "revolute" or self.type == "free2d":
transformed_joint_pts = torch.matmul(parent_rot_mtx, self.pos.view(3 ,1).contiguous()).view(3).contiguous() + parent_trans_vec
# else:
self.transformed_joint_pts = transformed_joint_pts ### get self transformed joint pts here ###
return transformed_joint_pts
# if self.joint_idx >= 0:
# cur_joint_state = state_vals[self.joint_idx]
# else:
# cur_joint_state = self.state # state #
# # use the state to transform them # # transform ### transform the state ##
# # use the state to transform them # # transform them for the state # transform for the state #
# if self.type == "revolute":
# # print(f"computing transformation matrices with axis: {self.axis}, state: {self.state}")
# # rotation matrix from the axis angle #
# rot_mtx = rotation_matrix_from_axis_angle(self.axis, cur_joint_state)
# # rot_mtx(p - p_v) + p_v -> rot_mtx p - rot_mtx p_v + p_v
# # trans_vec = self.pos - np.matmul(rot_mtx, self.pos.reshape(3, 1)).reshape(3)
# # self.rot_mtx = np.copy(rot_mtx)
# # self.trans_vec = np.copy(trans_vec)
# trans_vec = self.pos - torch.matmul(rot_mtx, self.pos.view(3, 1)).view(3).contiguous()
# self.rot_mtx = rot_mtx
# self.trans_vec = trans_vec
# elif self.type == "free2d":
# cur_joint_state = state_vals # still only for the current scene #
# # cur_joint_state
# cur_joint_rot_val = state_vals[2]
# ### rot_mtx ### ### rot_mtx ###
# rot_mtx = plane_rotation_matrix_from_angle_xz(cur_joint_rot_val)
# # rot_mtx = plane_rotation_matrix_from_angle(cur_joint_rot_val) ### 2 x 2 rot matrix #
# # cur joint rot val #
# # rot mtx of the rotation
# # xy_val =
# # axis_rot_mtx
# # R_axis^T ( R R_axis (p) + trans (with the y-axis padded) )
# cur_trans_vec = torch.stack(
# [state_vals[0], torch.zeros_like(state_vals[0]), state_vals[1]], dim=0
# )
# # cur_trans_vec #
# rot_mtx = torch.matmul(self.axis_rot_mtx.transpose(1, 0), torch.matmul(rot_mtx, self.axis_rot_mtx))
# trans_vec = torch.matmul(self.axis_rot_mtx.transpose(1, 0), cur_trans_vec.unsqueeze(-1).contiguous()).squeeze(-1).contiguous() + self.pos
# self.rot_mtx = rot_mtx
# self.trans_vec = trans_vec ## rot_mtx and trans_vec #
# else:
# ### TODO: implement transformations for joints in other types ###
# pass
# return self.rot_mtx, self.trans_vec
## fixed joint -> translation and rotation ##
## revolute joint -> can be actuated ##
## set states and compute the transfromations in a top-to-down manner ##
## trnasform the robot -> a list of qs ##
## a list of qs ##
## transform from the root of the robot; pass qs from the root to the leaf node ##
## visual meshes or visual meshes from the basic description of robots ##
## visual meshes; or visual points ##
## visual meshes -> transform them into the visual density values here ##
## visual meshes -> transform them into the ## into the visual counterparts ##
## ## visual meshes -> ## ## ##
# <body name="body0" type="mesh" filename="hand/body0.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.700000 0.700000 0.700000 1"/>
class Body:
def __init__(self, name, body_type, filename, pos, quat, transform_type, density, mu, rgba, radius, args) -> None:
self.name = name
self.body_type = body_type
### for mesh object ###
self.filename = filename
self.args = args
self.pos = pos
self.quat = quat
self.transform_type = transform_type
self.density = density
self.mu = mu
self.rgba = rgba
### for sphere object ###
self.radius = radius
## or vertices here #
## pass them to the child and treat them as the parent transformation ##
self.visual_pts_ref = None
self.visual_faces_ref = None
self.visual_pts = None ## visual pts and
self.body_name_to_main_axis = get_body_name_to_main_axis() ### get the body name to main axis here #
self.get_visual_counterparts()
def update_radius(self,):
self.radius.data = self.radius.data - self.radius.grad.data
self.radius.grad.data = self.radius.grad.data * 0.
def update_xml_file(self,):
xml_content_with_flexible_radius = f"""<redmax model="hand">
<!-- 1) change the damping value here? -->
<!-- 2) change the center of mass -->
<option integrator="BDF2" timestep="0.01" gravity="0. 0. -0.000098"/>
<ground pos="0 0 -10" normal="0 0 1"/>
<default>
<general_primitive_contact kn="1e6" kt="1e3" mu="0.8" damping="3e1" />
</default>
<robot>
<link name="link0">
<joint name="joint0" type="fixed" pos="0 0 0" quat="1 0 0 0" frame="WORLD"/>
<body name="body0" type="mesh" filename="hand/body0.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.700000 0.700000 0.700000 1"/>
<link name="link1">
<joint name="joint1" type="revolute" axis="0.000000 0.000000 -1.000000" pos="-3.300000 -5.689700 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body1" type="mesh" filename="hand/body1.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.600000 0.600000 0.600000 1"/>
<link name="link2">
<joint name="joint2" type="revolute" axis="1.000000 0.000000 0.000000" pos="-3.300000 -7.680000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body2" type="mesh" filename="hand/body2.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.500000 0.500000 0.500000 1"/>
</link>
</link>
<link name="link3">
<!-- revolute joint -->
<joint name="joint3" type="revolute" axis="0.000000 0.000000 -1.000000" pos="4.689700 -4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body3" type="mesh" filename="hand/body3.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.600000 0.600000 0.600000 1"/>
<link name="link4">
<joint name="joint4" type="revolute" axis="0.000000 1.000000 0.000000" pos="6.680000 -4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body4" type="mesh" filename="hand/body4.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.500000 0.500000 0.500000 1"/>
<link name="link5">
<joint name="joint5" type="revolute" axis="0.000000 1.000000 0.000000" pos="11.080000 -4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body5" type="mesh" filename="hand/body5.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.400000 0.400000 0.400000 1"/>
<link name="link6">
<joint name="joint6" type="revolute" axis="0.000000 1.000000 0.000000" pos="15.480000 -4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body6" type="mesh" filename="hand/body6.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.300000 0.300000 0.300000 1"/>
</link>
</link>
</link>
</link>
<link name="link7">
<joint name="joint7" type="revolute" axis="0.000000 0.000000 -1.000000" pos="4.689700 -1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body7" type="mesh" filename="hand/body7.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.600000 0.600000 0.600000 1"/>
<link name="link8">
<joint name="joint8" type="revolute" axis="0.000000 1.000000 0.000000" pos="6.680000 -1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body8" type="mesh" filename="hand/body8.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.500000 0.500000 0.500000 1"/>
<link name="link9">
<joint name="joint9" type="revolute" axis="0.000000 1.000000 0.000000" pos="11.080000 -1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body9" type="mesh" filename="hand/body9.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.400000 0.400000 0.400000 1"/>
<link name="link10">
<joint name="joint10" type="revolute" axis="0.000000 1.000000 0.000000" pos="15.480000 -1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body10" type="mesh" filename="hand/body10.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.300000 0.300000 0.300000 1"/>
</link>
</link>
</link>
</link>
<link name="link11">
<joint name="joint11" type="revolute" axis="0.000000 0.000000 -1.000000" pos="4.689700 1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body11" type="mesh" filename="hand/body11.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.600000 0.600000 0.600000 1"/>
<link name="link12">
<joint name="joint12" type="revolute" axis="0.000000 1.000000 0.000000" pos="6.680000 1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body12" type="mesh" filename="hand/body12.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.500000 0.500000 0.500000 1"/>
<link name="link13">
<joint name="joint13" type="revolute" axis="0.000000 1.000000 0.000000" pos="11.080000 1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body13" type="mesh" filename="hand/body13.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.400000 0.400000 0.400000 1"/>
<link name="link14">
<joint name="joint14" type="revolute" axis="0.000000 1.000000 0.000000" pos="15.480000 1.475000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body14" type="mesh" filename="hand/body14.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.300000 0.300000 0.300000 1"/>
</link>
</link>
</link>
</link>
<link name="link15">
<joint name="joint15" type="revolute" axis="0.000000 0.000000 -1.000000" pos="4.689700 4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body15" type="mesh" filename="hand/body15.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.600000 0.600000 0.600000 1"/>
<link name="link16">
<joint name="joint16" type="revolute" axis="0.000000 1.000000 0.000000" pos="6.680000 4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body16" type="mesh" filename="hand/body16.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.500000 0.500000 0.500000 1"/>
<link name="link17">
<joint name="joint17" type="revolute" axis="0.000000 1.000000 0.000000" pos="11.080000 4.425000 0.000000" quat="1 0 0 0" frame="WORLD" damping="1e4"/>
<body name="body17" type="mesh" filename="hand/body17.obj" pos="0 0 0" quat="1 0 0 0" transform_type="OBJ_TO_WORLD" density="1" mu="0" rgba="0.400000 0.400000 0.400000 1"/>
</link>
</link>
</link>
</link>
</robot>
<robot>
<link name="sphere">
<joint name="sphere" type="free2d" pos = "10. 0.0 3.5" quat="1 -1 0 0" format="LOCAL" damping="0"/>
<body name="sphere" type="sphere" radius="{self.radius[0].detach().cpu().item()}" pos="0 0 0" quat="1 0 0 0" density="0.5" mu="0" texture="resources/textures/sphere.jpg"/>
</link>
</robot>
<contact>
<ground_contact body="sphere" kn="1e6" kt="1e3" mu="0.8" damping="3e1"/>
<general_primitive_contact general_body="body0" primitive_body="sphere"/>
<general_primitive_contact general_body="body1" primitive_body="sphere"/>
<general_primitive_contact general_body="body2" primitive_body="sphere"/>
<general_primitive_contact general_body="body3" primitive_body="sphere"/>
<general_primitive_contact general_body="body4" primitive_body="sphere"/>
<general_primitive_contact general_body="body5" primitive_body="sphere"/>
<general_primitive_contact general_body="body6" primitive_body="sphere"/>
<general_primitive_contact general_body="body7" primitive_body="sphere"/>
<general_primitive_contact general_body="body8" primitive_body="sphere"/>
<general_primitive_contact general_body="body9" primitive_body="sphere"/>
<general_primitive_contact general_body="body10" primitive_body="sphere"/>
<general_primitive_contact general_body="body11" primitive_body="sphere"/>
<general_primitive_contact general_body="body12" primitive_body="sphere"/>
<general_primitive_contact general_body="body13" primitive_body="sphere"/>
<general_primitive_contact general_body="body14" primitive_body="sphere"/>
<general_primitive_contact general_body="body15" primitive_body="sphere"/>
<general_primitive_contact general_body="body16" primitive_body="sphere"/>
<general_primitive_contact general_body="body17" primitive_body="sphere"/>
</contact>
<actuator>
<motor joint="joint1" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint2" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint3" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint4" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint5" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint6" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint7" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint8" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint9" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint10" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint11" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint12" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint13" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint14" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint15" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint16" ctrl="force" ctrl_range="-3e5 3e5"/>
<motor joint="joint17" ctrl="force" ctrl_range="-3e5 3e5"/>
</actuator>
</redmax>
"""
xml_loading_fn = "/home/xueyi/diffsim/DiffHand/assets/hand_sphere_free_sphere_geo_test.xml"
with open(xml_loading_fn, "w") as wf:
wf.write(xml_content_with_flexible_radius)
wf.close()
### get visual pts colorrs ### ###
def get_visual_pts_colors(self, ):
tot_visual_pts_nn = self.visual_pts_ref.size(0)
# self.pts_rgba = [torch.from_numpy(self.rgba).float().cuda(self.args.th_cuda_idx) for _ in range(tot_visual_pts_nn)] # total visual pts nn
self.pts_rgba = [torch.tensor(self.rgba.data).cuda(self.args.th_cuda_idx) for _ in range(tot_visual_pts_nn)] # total visual pts nn skeletong
self.pts_rgba = torch.stack(self.pts_rgba, dim=0) #
return self.pts_rgba
def get_visual_counterparts(self,):
### TODO: implement this for visual counterparts ### mid line regression and name to body mapping relations --- for each body, how to calculate the midline and other properties?
######## get body type ########## get visual midline of the input mesh and the mesh vertices? ######## # skeleton of the hand -> 21 points ? retarget from this hand to the mano hand and use the mano hand priors?
if self.body_type == "sphere":
filename = "/home/xueyi/diffsim/DiffHand/examples/save_res/hand_sphere_demo/meshes/18.obj"
if not os.path.exists(filename):
filename = "/data/xueyi/diffsim/DiffHand/assets/18.obj"
else:
filename = self.filename
rt_asset_path = "/home/xueyi/diffsim/DiffHand/assets" ### assets folder ###
if not os.path.exists(rt_asset_path):
rt_asset_path = "/data/xueyi/diffsim/DiffHand/assets"
filename = os.path.join(rt_asset_path, filename)
body_mesh = trimesh.load(filename, process=False)
# verts = np.array(body_mesh.vertices)
# faces = np.array(body_mesh.faces, dtype=np.long)
# self.visual_pts_ref = np.copy(verts) ## verts ##
# self.visual_faces_ref = np.copy(faces) ## faces
# self.visual_pts_ref #
#### body_mesh.vertices ####
# verts = torch.tensor(body_mesh.vertices, dtype=torch.float32).cuda(self.args.th_cuda_idx)
# faces = torch.tensor(body_mesh.faces, dtype=torch.long).cuda(self.args.th_cuda_idx)
#### body_mesh.vertices ####
# self.pos = nn.Parameter(
# torch.tensor([0., 0., 0.], dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
# )
self.pos = nn.Parameter(
torch.tensor(self.pos.detach().cpu().tolist(), dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
)
### Step 1 ### -> set the pos to the correct initial pose ###
self.radius = nn.Parameter(
torch.tensor([self.args.initial_radius], dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
)
### visual pts ref ### ## body_mesh.vertices -> #
self.visual_pts_ref = torch.tensor(body_mesh.vertices, dtype=torch.float32).cuda(self.args.th_cuda_idx)
# if self.name == "sphere":
# self.visual_pts_ref = self.visual_pts_ref / 2. # the initial radius
# self.visual_pts_ref = self.visual_pts_ref * self.radius ## multiple the initla radius #
# self.visual_pts_ref = nn.Parameter(
# torch.tensor(body_mesh.vertices, dtype=torch.float32, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
# )
# self.visual_faces_ref = nn.Parameter(
# torch.tensor(body_mesh.faces, dtype=torch.long, requires_grad=True).cuda(self.args.th_cuda_idx), requires_grad=True
# )
self.visual_faces_ref = torch.tensor(body_mesh.faces, dtype=torch.long).cuda(self.args.th_cuda_idx)
# body_name_to_main_axis
# body_name_to_main_axis for the body_name_to_main_axis #
# visual_faces_ref #
# visual_pts_ref #
minn_pts, _ = torch.min(self.visual_pts_ref, dim=0) ### get the visual pts minn ###
maxx_pts, _ = torch.max(self.visual_pts_ref, dim=0) ### visual pts maxx ###
mean_pts = torch.mean(self.visual_pts_ref, dim=0) ### mean_pts of the mean_pts ###
if self.name in self.body_name_to_main_axis:
cur_main_axis = self.body_name_to_main_axis[self.name] ## get the body name ##
if cur_main_axis == -2:
main_axis_pts = minn_pts[1] # the main axis pts
full_main_axis_pts = torch.tensor([mean_pts[0], main_axis_pts, mean_pts[2]], dtype=torch.float32).cuda(self.args.th_cuda_idx)
elif cur_main_axis == 1:
main_axis_pts = maxx_pts[0] # the maxx axis pts
full_main_axis_pts = torch.tensor([main_axis_pts, mean_pts[1], mean_pts[2]], dtype=torch.float32).cuda(self.args.th_cuda_idx)
self.full_main_axis_pts_ref = full_main_axis_pts
else:
self.full_main_axis_pts_ref = mean_pts.clone() ### get the mean pts ###
# mean_pts
# main_axis_pts =
# self.visual_pts_ref = verts #
# self.visual_faces_ref = faces #
# get visual points colors # the color should be an optimizable property # # # or init visual point colors here ## # or init visual point colors here #
# simulatoable assets ## for the
def transform_visual_pts_ref(self,):
if self.name == "sphere":
visual_pts_ref = self.visual_pts_ref / 2. #
visual_pts_ref = visual_pts_ref * self.radius
else:
visual_pts_ref = self.visual_pts_ref
return visual_pts_ref
def transform_visual_pts(self, rot_mtx, trans_vec):
visual_pts_ref = self.transform_visual_pts_ref()
# rot_mtx: 3 x 3 numpy array
# trans_vec: 3 numpy array
# print(f"transforming body with rot_mtx: {rot_mtx} and trans_vec: {trans_vec}")
# self.visual_pts = np.matmul(rot_mtx, self.visual_pts_ref.T).T + trans_vec.reshape(1, 3) # reshape #
# print(f"rot_mtx: {rot_mtx}, trans_vec: {trans_vec}")
self.visual_pts = torch.matmul(rot_mtx, visual_pts_ref.transpose(1, 0)).transpose(1, 0) + trans_vec.unsqueeze(0)
# full_main_axis_pts ->
self.full_main_axis_pts = torch.matmul(rot_mtx, self.full_main_axis_pts_ref.unsqueeze(-1)).contiguous().squeeze(-1) + trans_vec
self.full_main_axis_pts = self.full_main_axis_pts.unsqueeze(0)
return self.visual_pts
def get_tot_transformed_joints(self, transformed_joints):
if self.name in self.body_name_to_main_axis:
transformed_joints.append(self.full_main_axis_pts)
return transformed_joints
def get_nn_pts(self,):
self.nn_pts = self.visual_pts_ref.size(0)
return self.nn_pts
def set_args(self, args):
self.args = args
def clear_grad(self, ):
if self.pos.grad is not None:
self.pos.grad.data = self.pos.grad.data * 0.
if self.radius.grad is not None:
self.radius.grad.data = self.radius.grad.data * 0.
# get the visual counterparts of the boyd mesh or elements #
# xyz attribute ## ## xyz attribute #
# use get_name_to_visual_pts
# use get_name_to_visual_pts_faces to get the transformed visual pts and faces #
class Link:
def __init__(self, name, joint: Joint, body: Body, children, args) -> None:
self.joint = joint
self.body = body
self.children = children
self.name = name
self.args = args
# joint # parent_rot_mtx, parent_trans_vec
self.parent_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True)
self.parent_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True)
self.curr_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True)
self.curr_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True)
#
self.tot_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True)
self.tot_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(self.args.th_cuda_idx), requires_grad=True) ## torch zeros #
def print_grads(self, ): ### print grads here ###
print(f"parent_rot_mtx: {self.parent_rot_mtx.grad}")
print(f"parent_trans_vec: {self.parent_trans_vec.grad}")
print(f"curr_rot_mtx: {self.curr_rot_mtx.grad}")
print(f"curr_trans_vec: {self.curr_trans_vec.grad}")
print(f"tot_rot_mtx: {self.tot_rot_mtx.grad}")
print(f"tot_trans_vec: {self.tot_trans_vec.grad}")
print(f"Joint")
self.joint.print_grads()
for cur_link in self.children:
cur_link.print_grads()
def set_state(self, name_to_state):
self.joint.set_state(name_to_state=name_to_state)
for child_link in self.children:
child_link.set_state(name_to_state)
def set_state_via_vec(self, state_vec): #
self.joint.set_state_via_vec(state_vec)
for child_link in self.children:
child_link.set_state_via_vec(state_vec)
# if self.joint_idx >= 0:
# self.state = state_vec[self.joint_idx]
##
def get_tot_transformed_joints(self, transformed_joints):
cur_joint_transformed_pts = self.joint.transformed_joint_pts.unsqueeze(0) ### 3 pts
transformed_joints.append(cur_joint_transformed_pts)
transformed_joints = self.body.get_tot_transformed_joints(transformed_joints)
# if self.joint.name
for cur_link in self.children:
transformed_joints = cur_link.get_tot_transformed_joints(transformed_joints)
return transformed_joints
def compute_transformation_via_state_vecs(self, state_vals, parent_rot_mtx, parent_trans_vec, visual_pts_list):
# state vecs and rot mtx # state vecs #####
joint_rot_mtx, joint_trans_vec = self.joint.compute_transformation_via_state_vals(state_vals=state_vals)
self.curr_rot_mtx = joint_rot_mtx
self.curr_trans_vec = joint_trans_vec
self.joint.transform_joints_via_parent_rot_trans_infos(parent_rot_mtx=parent_rot_mtx, parent_trans_vec=parent_trans_vec) ## get rot and trans mtx and vecs ###
tot_parent_rot_mtx = torch.matmul(parent_rot_mtx, joint_rot_mtx)
tot_parent_trans_vec = torch.matmul(parent_rot_mtx, joint_trans_vec.unsqueeze(-1)).view(3) + parent_trans_vec
self.tot_rot_mtx = tot_parent_rot_mtx
self.tot_trans_vec = tot_parent_trans_vec
# self.tot_rot_mtx = np.copy(tot_parent_rot_mtx)
# self.tot_trans_vec = np.copy(tot_parent_trans_vec)
### visual_pts_list for recording visual pts ###
cur_body_visual_pts = self.body.transform_visual_pts(rot_mtx=self.tot_rot_mtx, trans_vec=self.tot_trans_vec)
visual_pts_list.append(cur_body_visual_pts)
for cur_link in self.children:
# cur_link.parent_rot_mtx = np.copy(tot_parent_rot_mtx) ### set children parent rot mtx and the trans vec
# cur_link.parent_trans_vec = np.copy(tot_parent_trans_vec) ##
cur_link.parent_rot_mtx = tot_parent_rot_mtx ### set children parent rot mtx and the trans vec #
cur_link.parent_trans_vec = tot_parent_trans_vec ##
# cur_link.compute_transformation() ## compute self's transformations
cur_link.compute_transformation_via_state_vecs(state_vals, tot_parent_rot_mtx, tot_parent_trans_vec, visual_pts_list)
def get_visual_pts_rgba_values(self, pts_rgba_vals_list):
cur_body_visual_rgba_vals = self.body.get_visual_pts_colors()
pts_rgba_vals_list.append(cur_body_visual_rgba_vals)
for cur_link in self.children:
cur_link.get_visual_pts_rgba_values(pts_rgba_vals_list)
def compute_transformation(self,):
self.joint.compute_transformation()
# self.curr_rot_mtx = np.copy(self.joint.rot_mtx)
# self.curr_trans_vec = np.copy(self.joint.trans_vec)
self.curr_rot_mtx = self.joint.rot_mtx
self.curr_trans_vec = self.joint.trans_vec
# rot_p (rot_c p + trans_c) + trans_p #
# rot_p rot_c p + rot_p trans_c + trans_p #
#### matmul ####
# tot_parent_rot_mtx = np.matmul(self.parent_rot_mtx, self.curr_rot_mtx)
# tot_parent_trans_vec = np.matmul(self.parent_rot_mtx, self.curr_trans_vec.reshape(3, 1)).reshape(3) + self.parent_trans_vec
tot_parent_rot_mtx = torch.matmul(self.parent_rot_mtx, self.curr_rot_mtx)
tot_parent_trans_vec = torch.matmul(self.parent_rot_mtx, self.curr_trans_vec.unsqueeze(-1)).view(3) + self.parent_trans_vec
self.tot_rot_mtx = tot_parent_rot_mtx
self.tot_trans_vec = tot_parent_trans_vec
# self.tot_rot_mtx = np.copy(tot_parent_rot_mtx)
# self.tot_trans_vec = np.copy(tot_parent_trans_vec)
for cur_link in self.children:
# cur_link.parent_rot_mtx = np.copy(tot_parent_rot_mtx) ### set children parent rot mtx and the trans vec
# cur_link.parent_trans_vec = np.copy(tot_parent_trans_vec) ##
cur_link.parent_rot_mtx = tot_parent_rot_mtx ### set children parent rot mtx and the trans vec #
cur_link.parent_trans_vec = tot_parent_trans_vec ##
cur_link.compute_transformation() ## compute self's transformations
def get_name_to_visual_pts_faces(self, name_to_visual_pts_faces):
# transform_visual_pts # ## rot_mt
self.body.transform_visual_pts(rot_mtx=self.tot_rot_mtx, trans_vec=self.tot_trans_vec)
name_to_visual_pts_faces[self.body.name] = {"pts": self.body.visual_pts, "faces": self.body.visual_faces_ref}
for cur_link in self.children:
cur_link.get_name_to_visual_pts_faces(name_to_visual_pts_faces) ## transform the pts faces
def get_visual_pts_list(self, visual_pts_list):
# transform_visual_pts # ## rot_mt
self.body.transform_visual_pts(rot_mtx=self.tot_rot_mtx, trans_vec=self.tot_trans_vec)
visual_pts_list.append(self.body.visual_pts) # body template #
# name_to_visual_pts_faces[self.body.name] = {"pts": self.body.visual_pts, "faces": self.body.visual_faces_ref}
for cur_link in self.children:
# cur_link.get_name_to_visual_pts_faces(name_to_visual_pts_faces) ## transform the pts faces
cur_link.get_visual_pts_list(visual_pts_list)
def set_joint_idx(self, joint_name_to_idx):
self.joint.set_joint_idx(joint_name_to_idx)
for cur_link in self.children:
cur_link.set_joint_idx(joint_name_to_idx)
# if self.name in joint_name_to_idx:
# self.joint_idx = joint_name_to_idx[self.name]
def get_nn_pts(self,):
nn_pts = 0
nn_pts += self.body.get_nn_pts()
for cur_link in self.children:
nn_pts += cur_link.get_nn_pts()
self.nn_pts = nn_pts
return self.nn_pts
def clear_grads(self,):
if self.parent_rot_mtx.grad is not None:
self.parent_rot_mtx.grad.data = self.parent_rot_mtx.grad.data * 0.
if self.parent_trans_vec.grad is not None:
self.parent_trans_vec.grad.data = self.parent_trans_vec.grad.data * 0.
if self.curr_rot_mtx.grad is not None:
self.curr_rot_mtx.grad.data = self.curr_rot_mtx.grad.data * 0.
if self.curr_trans_vec.grad is not None:
self.curr_trans_vec.grad.data = self.curr_trans_vec.grad.data * 0.
if self.tot_rot_mtx.grad is not None:
self.tot_rot_mtx.grad.data = self.tot_rot_mtx.grad.data * 0.
if self.tot_trans_vec.grad is not None:
self.tot_trans_vec.grad.data = self.tot_trans_vec.grad.data * 0.
# print(f"parent_rot_mtx: {self.parent_rot_mtx.grad}")
# print(f"parent_trans_vec: {self.parent_trans_vec.grad}")
# print(f"curr_rot_mtx: {self.curr_rot_mtx.grad}")
# print(f"curr_trans_vec: {self.curr_trans_vec.grad}")
# print(f"tot_rot_mtx: {self.tot_rot_mtx.grad}")
# print(f"tot_trans_vec: {self.tot_trans_vec.grad}")
# print(f"Joint")
self.joint.clear_grads()
self.body.clear_grad()
for cur_link in self.children:
cur_link.clear_grads()
def set_args(self, args):
self.args = args
for cur_link in self.children:
cur_link.set_args(args)
class Robot: # robot and the robot #
def __init__(self, children_links, args) -> None:
self.children = children_links
self.args = args
def set_state(self, name_to_state):
for cur_link in self.children:
cur_link.set_state(name_to_state)
def compute_transformation(self,):
for cur_link in self.children:
cur_link.compute_transformation()
def get_name_to_visual_pts_faces(self, name_to_visual_pts_faces):
for cur_link in self.children:
cur_link.get_name_to_visual_pts_faces(name_to_visual_pts_faces)
def get_visual_pts_list(self, visual_pts_list):
for cur_link in self.children:
cur_link.get_visual_pts_list(visual_pts_list)
def set_joint_idx(self, joint_name_to_idx):
for cur_link in self.children:
cur_link.set_joint_idx(joint_name_to_idx) ### set joint idx ###
def set_state_via_vec(self, state_vec): ### set the state vec for the state vec ###
for cur_link in self.children: ### set the state vec for the state vec ###
cur_link.set_state_via_vec(state_vec)
# self.joint.set_state_via_vec(state_vec)
# for child_link in self.children:
# child_link.set_state_via_vec(state_vec)
# get_tot_transformed_joints
def get_tot_transformed_joints(self, transformed_joints):
for cur_link in self.children: #
transformed_joints = cur_link.get_tot_transformed_joints(transformed_joints)
return transformed_joints
def get_nn_pts(self):
nn_pts = 0
for cur_link in self.children:
nn_pts += cur_link.get_nn_pts()
self.nn_pts = nn_pts
return self.nn_pts
def set_args(self, args):
self.args = args
for cur_link in self.children: ## args ##
cur_link.set_args(args)
def print_grads(self):
for cur_link in self.children:
cur_link.print_grads()
def clear_grads(self,): ## clear grads ##
for cur_link in self.children:
cur_link.clear_grads()
def compute_transformation_via_state_vecs(self, state_vals, visual_pts_list):
# parent_rot_mtx, parent_trans_vec
for cur_link in self.children:
cur_link.compute_transformation_via_state_vecs(state_vals, cur_link.parent_rot_mtx, cur_link.parent_trans_vec, visual_pts_list)
return visual_pts_list
# get_visual_pts_rgba_values(self, pts_rgba_vals_list):
def get_visual_pts_rgba_values(self, pts_rgba_vals_list):
for cur_link in self.children:
cur_link.get_visual_pts_rgba_values(pts_rgba_vals_list)
return pts_rgba_vals_list ## compute pts rgba vals list ##
def parse_nparray_from_string(strr, args):
vals = strr.split(" ")
vals = [float(val) for val in vals]
vals = np.array(vals, dtype=np.float32)
vals = torch.from_numpy(vals).float()
## vals ##
vals = nn.Parameter(vals.cuda(args.th_cuda_idx), requires_grad=True)
return vals
### parse link data ###
def parse_link_data(link, args):
link_name = link.attrib["name"]
# print(f"parsing link: {link_name}") ## joints body meshes #
joint = link.find("./joint")
joint_name = joint.attrib["name"]
joint_type = joint.attrib["type"]
if joint_type in ["revolute"]: ## a general xml parser here?
axis = joint.attrib["axis"]
axis = parse_nparray_from_string(axis, args=args)
else:
axis = None
pos = joint.attrib["pos"] #
pos = parse_nparray_from_string(pos, args=args)
quat = joint.attrib["quat"]
quat = parse_nparray_from_string(quat, args=args)
try:
frame = joint.attrib["frame"]
except:
frame = "WORLD"
if joint_type not in ["fixed"]:
damping = joint.attrib["damping"]
damping = float(damping)
else:
damping = 0.0
cur_joint = Joint(joint_name, joint_type, axis, pos, quat, frame, damping, args=args)
body = link.find("./body")
body_name = body.attrib["name"]
body_type = body.attrib["type"]
if body_type == "mesh":
filename = body.attrib["filename"]
else:
filename = ""
if body_type == "sphere":
radius = body.attrib["radius"]
radius = float(radius)
else:
radius = 0.
pos = body.attrib["pos"]
pos = parse_nparray_from_string(pos, args=args)
quat = body.attrib["quat"]
quat = joint.attrib["quat"]
try:
transform_type = body.attrib["transform_type"]
except:
transform_type = "OBJ_TO_WORLD"
density = body.attrib["density"]
density = float(density)
mu = body.attrib["mu"]
mu = float(mu)
try: ## rgba ##
rgba = body.attrib["rgba"]
rgba = parse_nparray_from_string(rgba, args=args)
except:
rgba = np.zeros((4,), dtype=np.float32)
cur_body = Body(body_name, body_type, filename, pos, quat, transform_type, density, mu, rgba, radius, args=args)
children_link = []
links = link.findall("./link")
for child_link in links: #
cur_child_link = parse_link_data(child_link, args=args)
children_link.append(cur_child_link)
link_name = link.attrib["name"]
link_obj = Link(link_name, joint=cur_joint, body=cur_body, children=children_link, args=args)
return link_obj
def parse_data_from_xml(xml_fn, args):
tree = ElementTree()
tree.parse(xml_fn)
### get total robots ###
robots = tree.findall("./robot")
i_robot = 0
tot_robots = []
for cur_robot in robots:
print(f"Getting robot: {i_robot}")
i_robot += 1
cur_links = cur_robot.findall("./link")
# i_link = 0
cur_robot_links = []
for cur_link in cur_links: ## child of the link ##
### a parse link util -> the child of the link is composed of (the joint; body; and children links (with children or with no child here))
# cur_link_name = cur_link.attrib["name"]
# print(f"Getting link: {i_link} with name: {cur_link_name}")
# i_link += 1 ##
cur_robot_links.append(parse_link_data(cur_link, args=args))
cur_robot_obj = Robot(cur_robot_links, args=args)
tot_robots.append(cur_robot_obj)
tot_actuators = []
actuators = tree.findall("./actuator/motor")
joint_nm_to_joint_idx = {}
i_act = 0
for cur_act in actuators:
cur_act_joint_nm = cur_act.attrib["joint"]
joint_nm_to_joint_idx[cur_act_joint_nm] = i_act
i_act += 1 ### add the act ###
tot_robots[0].set_joint_idx(joint_nm_to_joint_idx) ### set joint idx here ### # tot robots #
tot_robots[0].get_nn_pts()
tot_robots[1].get_nn_pts()
return tot_robots
def get_name_to_state_from_str(states_str):
tot_states = states_str.split(" ")
tot_states = [float(cur_state) for cur_state in tot_states]
joint_name_to_state = {}
for i in range(len(tot_states)):
cur_joint_name = f"joint{i + 1}"
cur_joint_state = tot_states[i]
joint_name_to_state[cur_joint_name] = cur_joint_state
return joint_name_to_state
def create_zero_states():
nn_joints = 17
joint_name_to_state = {}
for i_j in range(nn_joints):
cur_joint_name = f"joint{i_j + 1}"
joint_name_to_state[cur_joint_name] = 0.
return joint_name_to_state
# [6.96331033e-17 3.54807679e-06 1.74046190e-15 2.66367417e-05
# 1.22444894e-05 3.38976792e-06 1.46917635e-15 2.66367383e-05
# 1.22444882e-05 3.38976786e-06 1.97778813e-15 2.66367383e-05
# 1.22444882e-05 3.38976786e-06 4.76033293e-16 1.26279884e-05
# 3.51189993e-06 0.00000000e+00 4.89999978e-03 0.00000000e+00]
def rotation_matrix_from_axis_angle_np(axis, angle): # rotation_matrix_from_axis_angle ->
sin_ = np.sin(angle) # ti.math.sin(angle)
cos_ = np.cos(angle) # ti.math.cos(angle)
# sin_ = torch.sin(angle) # ti.math.sin(angle)
# cos_ = torch.cos(angle) # ti.math.cos(angle)
u_x, u_y, u_z = axis[0], axis[1], axis[2]
u_xx = u_x * u_x
u_yy = u_y * u_y
u_zz = u_z * u_z
u_xy = u_x * u_y
u_xz = u_x * u_z
u_yz = u_y * u_z ##
row_a = np.stack(
[cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], axis=0
)
# print(f"row_a: {row_a.size()}")
row_b = np.stack(
[u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], axis=0
)
# print(f"row_b: {row_b.size()}")
row_c = np.stack(
[u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], axis=0
)
# print(f"row_c: {row_c.size()}")
### rot_mtx for the rot_mtx ###
rot_mtx = np.stack(
[row_a, row_b, row_c], axis=-1 ### rot_matrix of he matrix ##
)
return rot_mtx
def rotation_matrix_from_axis_angle(axis, angle): # rotation_matrix_from_axis_angle ->
# sin_ = np.sin(angle) # ti.math.sin(angle)
# cos_ = np.cos(angle) # ti.math.cos(angle)
sin_ = torch.sin(angle) # ti.math.sin(angle)
cos_ = torch.cos(angle) # ti.math.cos(angle)
u_x, u_y, u_z = axis[0], axis[1], axis[2]
u_xx = u_x * u_x
u_yy = u_y * u_y
u_zz = u_z * u_z
u_xy = u_x * u_y
u_xz = u_x * u_z
u_yz = u_y * u_z ##
row_a = torch.stack(
[cos_ + u_xx * (1 - cos_), u_xy * (1. - cos_) + u_z * sin_, u_xz * (1. - cos_) - u_y * sin_], dim=0
)
# print(f"row_a: {row_a.size()}")
row_b = torch.stack(
[u_xy * (1. - cos_) - u_z * sin_, cos_ + u_yy * (1. - cos_), u_yz * (1. - cos_) + u_x * sin_], dim=0
)
# print(f"row_b: {row_b.size()}")
row_c = torch.stack(
[u_xz * (1. - cos_) + u_y * sin_, u_yz * (1. - cos_) - u_x * sin_, cos_ + u_zz * (1. - cos_)], dim=0
)
# print(f"row_c: {row_c.size()}")
### rot_mtx for the rot_mtx ###
rot_mtx = torch.stack(
[row_a, row_b, row_c], dim=-1 ### rot_matrix of he matrix ##
)
return rot_mtx
def get_camera_to_world_poses(n=10, ):
## sample from the upper half sphere ##
# theta and phi for the
theta = np.random.uniform(low=0.0, high=1.0, size=(n,)) * np.pi * 2. # xz palne #
phi = np.random.uniform(low=-1.0, high=0.0, size=(n,)) * np.pi ## [-0.5 \pi, 0.5 \pi] ## negative pi to the original pi
# theta = torch.from_numpy(theta).float().cuda()
tot_c2w_matrix = []
for i_n in range(n):
# y_rot_vec = torch.tensor([0., 1., 0.]).float().cuda(th_cuda_idx)
# y_rot_mtx = load_utils.rotation_matrix_from_axis_angle(rot_vec, rot_angle)
z_axis_rot_axis = np.array([0, 0, 1.], dtype=np.float32)
z_axis_rot_angle = np.pi - theta[i_n]
z_axis_rot_matrix = rotation_matrix_from_axis_angle_np(z_axis_rot_axis, z_axis_rot_angle)
rotated_plane_rot_axis_ori = np.array([1, -1, 0], dtype=np.float32)
rotated_plane_rot_axis_ori = rotated_plane_rot_axis_ori / np.sqrt(np.sum(rotated_plane_rot_axis_ori ** 2))
rotated_plane_rot_axis = np.matmul(z_axis_rot_matrix, rotated_plane_rot_axis_ori)
plane_rot_angle = phi[i_n]
plane_rot_matrix = rotation_matrix_from_axis_angle_np(rotated_plane_rot_axis, plane_rot_angle)
c2w_matrix = np.matmul(plane_rot_matrix, z_axis_rot_matrix)
c2w_trans_matrix = np.array(
[np.cos(theta[i_n]) * np.sin(phi[i_n]), np.sin(theta[i_n]) * np.sin(phi[i_n]), np.cos(phi[i_n])], dtype=np.float32
)
c2w_matrix = np.concatenate(
[c2w_matrix, c2w_trans_matrix.reshape(3, 1)], axis=-1
) ##c2w matrix
tot_c2w_matrix.append(c2w_matrix)
tot_c2w_matrix = np.stack(tot_c2w_matrix, axis=0)
return tot_c2w_matrix
def get_camera_to_world_poses_th(n=10, th_cuda_idx=0):
## sample from the upper half sphere ##
# theta and phi for the
theta = np.random.uniform(low=0.0, high=1.0, size=(n,)) * np.pi * 2. # xz palne #
phi = np.random.uniform(low=-1.0, high=0.0, size=(n,)) * np.pi ## [-0.5 \pi, 0.5 \pi] ## negative pi to the original pi
# n_total = 14
# n_xz = 14
# n_y = 7
# theta = [i_xz * 1.0 / float(n_xz) * np.pi * 2. for i_xz in range(n_xz)]
# phi = [i_y * (-1.0) / float(n_y) * np.pi for i_y in range(n_y)]
theta = torch.from_numpy(theta).float().cuda(th_cuda_idx)
phi = torch.from_numpy(phi).float().cuda(th_cuda_idx)
tot_c2w_matrix = []
for i_n in range(n): # if use veyr dense views like those
y_rot_angle = theta[i_n]
y_rot_vec = torch.tensor([0., 1., 0.]).float().cuda(th_cuda_idx)
y_rot_mtx = rotation_matrix_from_axis_angle(y_rot_vec, y_rot_angle)
x_axis = torch.tensor([1., 0., 0.]).float().cuda(th_cuda_idx)
y_rot_x_axis = torch.matmul(y_rot_mtx, x_axis.unsqueeze(-1)).squeeze(-1) ### y_rot_x_axis #
x_rot_angle = phi[i_n]
x_rot_mtx = rotation_matrix_from_axis_angle(y_rot_x_axis, x_rot_angle)
rot_mtx = torch.matmul(x_rot_mtx, y_rot_mtx)
xyz_offset = torch.tensor([0., 0., 1.5]).float().cuda(th_cuda_idx)
rot_xyz_offset = torch.matmul(rot_mtx, xyz_offset.unsqueeze(-1)).squeeze(-1).contiguous() + 0.5 ### 3 for the xyz offset
c2w_matrix = torch.cat(
[rot_mtx, rot_xyz_offset.unsqueeze(-1)], dim=-1
)
tot_c2w_matrix.append(c2w_matrix)
# z_axis_rot_axis = np.array([0, 0, 1.], dtype=np.float32)
# z_axis_rot_angle = np.pi - theta[i_n]
# z_axis_rot_matrix = rotation_matrix_from_axis_angle_np(z_axis_rot_axis, z_axis_rot_angle)
# rotated_plane_rot_axis_ori = np.array([1, -1, 0], dtype=np.float32)
# rotated_plane_rot_axis_ori = rotated_plane_rot_axis_ori / np.sqrt(np.sum(rotated_plane_rot_axis_ori ** 2))
# rotated_plane_rot_axis = np.matmul(z_axis_rot_matrix, rotated_plane_rot_axis_ori)
# plane_rot_angle = phi[i_n]
# plane_rot_matrix = rotation_matrix_from_axis_angle_np(rotated_plane_rot_axis, plane_rot_angle)
# c2w_matrix = np.matmul(plane_rot_matrix, z_axis_rot_matrix)
# c2w_trans_matrix = np.array(
# [np.cos(theta[i_n]) * np.sin(phi[i_n]), np.sin(theta[i_n]) * np.sin(phi[i_n]), np.cos(phi[i_n])], dtype=np.float32
# )
# c2w_matrix = np.concatenate(
# [c2w_matrix, c2w_trans_matrix.reshape(3, 1)], axis=-1
# ) ##c2w matrix
# tot_c2w_matrix.append(c2w_matrix)
# tot_c2w_matrix = np.stack(tot_c2w_matrix, axis=0)
tot_c2w_matrix = torch.stack(tot_c2w_matrix, dim=0)
return tot_c2w_matrix
def get_camera_to_world_poses_th_routine_1(n=7, th_cuda_idx=0):
## sample from the upper half sphere ##
# theta and phi for the
# theta = np.random.uniform(low=0.0, high=1.0, size=(n,)) * np.pi * 2. # xz palne #
# phi = np.random.uniform(low=-1.0, high=0.0, size=(n,)) * np.pi ## [-0.5 \pi, 0.5 \pi] ## negative pi to the original pi
# n_total = 14
n_xz = 2 * n # 14
n_y = n # 7
theta = [i_xz * 1.0 / float(n_xz) * np.pi * 2. for i_xz in range(n_xz)]
phi = [i_y * (-1.0) / float(n_y) * np.pi for i_y in range(n_y)]
theta = torch.tensor(theta).float().cuda(th_cuda_idx)
phi = torch.tensor(phi).float().cuda(th_cuda_idx)
# theta = torch.from_numpy(theta).float().cuda(th_cuda_idx)
# phi = torch.from_numpy(phi).float().cuda(th_cuda_idx)
tot_c2w_matrix = []
for i_theta in range(theta.size(0)):
for i_phi in range(phi.size(0)):
y_rot_angle = theta[i_theta]
y_rot_vec = torch.tensor([0., 1., 0.]).float().cuda(th_cuda_idx)
y_rot_mtx = rotation_matrix_from_axis_angle(y_rot_vec, y_rot_angle)
x_axis = torch.tensor([1., 0., 0.]).float().cuda(th_cuda_idx)
y_rot_x_axis = torch.matmul(y_rot_mtx, x_axis.unsqueeze(-1)).squeeze(-1) ### y_rot_x_axis #
x_rot_angle = phi[i_phi]
x_rot_mtx = rotation_matrix_from_axis_angle(y_rot_x_axis, x_rot_angle)
rot_mtx = torch.matmul(x_rot_mtx, y_rot_mtx)
xyz_offset = torch.tensor([0., 0., 1.5]).float().cuda(th_cuda_idx)
rot_xyz_offset = torch.matmul(rot_mtx, xyz_offset.unsqueeze(-1)).squeeze(-1).contiguous() + 0.5 ### 3 for the xyz offset
c2w_matrix = torch.cat(
[rot_mtx, rot_xyz_offset.unsqueeze(-1)], dim=-1
)
tot_c2w_matrix.append(c2w_matrix)
tot_c2w_matrix = torch.stack(tot_c2w_matrix, dim=0)
return tot_c2w_matrix
def get_camera_to_world_poses_th_routine_2(n=7, th_cuda_idx=0):
## sample from the upper half sphere ##
# theta and phi for the
# theta = np.random.uniform(low=0.0, high=1.0, size=(n,)) * np.pi * 2. # xz palne #
# phi = np.random.uniform(low=-1.0, high=0.0, size=(n,)) * np.pi ## [-0.5 \pi, 0.5 \pi] ## negative pi to the original pi
# n_total = 14
n_xz = 2 * n # 14
n_y = 2 * n # 7
theta = [i_xz * 1.0 / float(n_xz) * np.pi * 2. for i_xz in range(n_xz)]
# phi = [i_y * (-1.0) / float(n_y) * np.pi for i_y in range(n_y)]
phi = [i_y * (-1.0) / float(n_y) * np.pi * 2. for i_y in range(n_y)]
theta = torch.tensor(theta).float().cuda(th_cuda_idx)
phi = torch.tensor(phi).float().cuda(th_cuda_idx)
# theta = torch.from_numpy(theta).float().cuda(th_cuda_idx)
# phi = torch.from_numpy(phi).float().cuda(th_cuda_idx)
tot_c2w_matrix = []
for i_theta in range(theta.size(0)):
for i_phi in range(phi.size(0)):
y_rot_angle = theta[i_theta]
y_rot_vec = torch.tensor([0., 1., 0.]).float().cuda(th_cuda_idx)
y_rot_mtx = rotation_matrix_from_axis_angle(y_rot_vec, y_rot_angle)
x_axis = torch.tensor([1., 0., 0.]).float().cuda(th_cuda_idx)
y_rot_x_axis = torch.matmul(y_rot_mtx, x_axis.unsqueeze(-1)).squeeze(-1) ### y_rot_x_axis #
x_rot_angle = phi[i_phi]
x_rot_mtx = rotation_matrix_from_axis_angle(y_rot_x_axis, x_rot_angle)
rot_mtx = torch.matmul(x_rot_mtx, y_rot_mtx)
xyz_offset = torch.tensor([0., 0., 1.5]).float().cuda(th_cuda_idx)
rot_xyz_offset = torch.matmul(rot_mtx, xyz_offset.unsqueeze(-1)).squeeze(-1).contiguous() + 0.5 ### 3 for the xyz offset
c2w_matrix = torch.cat(
[rot_mtx, rot_xyz_offset.unsqueeze(-1)], dim=-1
)
tot_c2w_matrix.append(c2w_matrix)
tot_c2w_matrix = torch.stack(tot_c2w_matrix, dim=0)
return tot_c2w_matrix
if __name__=='__main__':
xml_fn = "/home/xueyi/diffsim/DiffHand/assets/hand_sphere.xml"
tot_robots = parse_data_from_xml(xml_fn=xml_fn)
# tot_robots =
active_optimized_states = """-0.00025872 -0.00025599 -0.00025296 -0.00022881 -0.00024449 -0.0002549 -0.00025296 -0.00022881 -0.00024449 -0.0002549 -0.00025296 -0.00022881 -0.00024449 -0.0002549 -0.00025694 -0.00024656 -0.00025556 0. 0.0049 0."""
active_optimized_states = """-1.10617972 -1.10742263 -1.06198363 -1.03212746 -1.05429142 -1.08617289 -1.05868192 -1.01624365 -1.04478191 -1.08260959 -1.06719107 -1.04082455 -1.05995886 -1.08674006 -1.09396691 -1.08965532 -1.10036577 -10.7117466 -3.62511998 1.49450353"""
# active_goal_optimized_states = """-1.10617972 -1.10742263 -1.0614858 -1.03189609 -1.05404354 -1.08610468 -1.05863293 -1.0174248 -1.04576456 -1.08297396 -1.06719107 -1.04082455 -1.05995886 -1.08674006 -1.09396691 -1.08965532 -1.10036577 -10.73396897 -3.68095432 1.50679285"""
active_optimized_states = """-0.42455298 -0.42570447 -0.40567708 -0.39798589 -0.40953955 -0.42025055 -0.37910662 -0.496165 -0.37664644 -0.41942727 -0.40596508 -0.3982109 -0.40959847 -0.42024905 -0.41835001 -0.41929961 -0.42365131 -1.18756073 -2.90337822 0.4224685"""
active_optimized_states = """-0.42442816 -0.42557961 -0.40366201 -0.3977891 -0.40947627 -0.4201424 -0.3799285 -0.3808375 -0.37953552 -0.42039598 -0.4058405 -0.39808804 -0.40947487 -0.42012458 -0.41822534 -0.41917521 -0.4235266 -0.87189658 -1.42093761 0.21977979"""
active_robot = tot_robots[0]
zero_states = create_zero_states()
active_robot.set_state(zero_states)
active_robot.compute_transformation()
name_to_visual_pts_surfaces = {}
active_robot.get_name_to_visual_pts_faces(name_to_visual_pts_surfaces)
print(len(name_to_visual_pts_surfaces))
sv_res_rt = "/home/xueyi/diffsim/DiffHand/examples/save_res"
sv_res_rt = os.path.join(sv_res_rt, "load_utils_test")
os.makedirs(sv_res_rt, exist_ok=True)
tmp_visual_res_sv_fn = os.path.join(sv_res_rt, f"res_with_zero_states.npy")
np.save(tmp_visual_res_sv_fn, name_to_visual_pts_surfaces)
print(f"tmp visual res saved to {tmp_visual_res_sv_fn}")
optimized_states = get_name_to_state_from_str(active_optimized_states)
active_robot.set_state(optimized_states)
active_robot.compute_transformation()
name_to_visual_pts_surfaces = {}
active_robot.get_name_to_visual_pts_faces(name_to_visual_pts_surfaces)
print(len(name_to_visual_pts_surfaces))
# sv_res_rt = "/home/xueyi/diffsim/DiffHand/examples/save_res"
# sv_res_rt = os.path.join(sv_res_rt, "load_utils_test")
# os.makedirs(sv_res_rt, exist_ok=True)
# tmp_visual_res_sv_fn = os.path.join(sv_res_rt, f"res_with_optimized_states.npy")
tmp_visual_res_sv_fn = os.path.join(sv_res_rt, f"active_ngoal_res_with_optimized_states_goal_n3.npy")
np.save(tmp_visual_res_sv_fn, name_to_visual_pts_surfaces)
print(f"tmp visual res with optimized states saved to {tmp_visual_res_sv_fn}")
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