quasi-physical-sims / models /dyn_model_act.py
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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
###
from scipy.spatial.transform import Rotation as R
DAMPING = 0.3
# DAMPING = 0.2
DAMPING = 0.0
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
)
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
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 update_quaternion(delta_angle, prev_quat):
s1 = 0
s2 = prev_quat[0]
v2 = prev_quat[1:]
v1 = delta_angle / 2
new_v = s1 * v2 + s2 * v1 + torch.cross(v1, v2)
new_s = s1 * s2 - torch.sum(v1 * v2)
new_quat = torch.cat([new_s.unsqueeze(0), new_v], dim=0)
return new_quat
def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor:
"""
Convert rotations given as Euler angles in radians to rotation matrices.
Args:
euler_angles: Euler angles in radians as tensor of shape (..., 3).
convention: Convention string of three uppercase letters from
{"X", "Y", and "Z"}.
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
raise ValueError("Invalid input euler angles.")
if len(convention) != 3:
raise ValueError("Convention must have 3 letters.")
if convention[1] in (convention[0], convention[2]):
raise ValueError(f"Invalid convention {convention}.")
for letter in convention:
if letter not in ("X", "Y", "Z"):
raise ValueError(f"Invalid letter {letter} in convention string.")
matrices = [
_axis_angle_rotation(c, e)
for c, e in zip(convention, torch.unbind(euler_angles, -1))
]
# return functools.reduce(torch.matmul, matrices)
return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2])
def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as quaternions to rotation matrices.
Args:
quaternions: quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
r, i, j, k = torch.unbind(quaternions, -1) # -1 for the quaternion matrix #
# pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
two_s = 2.0 / (quaternions * quaternions).sum(-1)
o = torch.stack(
(
1 - two_s * (j * j + k * k),
two_s * (i * j - k * r),
two_s * (i * k + j * r),
two_s * (i * j + k * r),
1 - two_s * (i * i + k * k),
two_s * (j * k - i * r),
two_s * (i * k - j * r),
two_s * (j * k + i * r),
1 - two_s * (i * i + j * j),
),
-1,
)
return o.reshape(quaternions.shape[:-1] + (3, 3))
## the optimization strategy: incremental optimization ##
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 # joint axis #
self.pos = pos # joint position #
self.quat = quat
self.frame = frame
self.damping = damping
self.args = args
# self.timestep_to_actions = {} # torques #
self.timestep_to_vels = {}
self.timestep_to_states = {}
self.init_pos = self.pos.clone()
#### only for the current state ####
self.state = nn.Parameter(
torch.tensor([1., 0., 0., 0.], dtype=torch.float32, requires_grad=True).cuda(), requires_grad=True
)
self.action = nn.Parameter(
torch.zeros((1,), dtype=torch.float32, requires_grad=True).cuda(), 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(), requires_grad=True)
self.trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32, requires_grad=True).cuda(), 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.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 = 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 #
# 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 compute_transformation_from_current_state(self):
# if self.joint_idx >= 0:
# cur_joint_state = state_vals[self.joint_idx]
# else:
cur_joint_state = self.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)
rot_mtx = quaternion_to_matrix(self.state)
# print(f"state: {self.state}, rot_mtx: {rot_mtx}")
# trans_vec = self.pos - torch.matmul(rot_mtx, self.pos.view(3, 1)).view(3).contiguous()
trans_vec = self.init_pos - torch.matmul(rot_mtx, self.init_pos.view(3, 1)).view(3).contiguous()
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec
elif self.type == "free2d":
state_vals = cur_joint_state
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 #
# 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
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
self.pos = torch.matmul(parent_rot_mtx, self.init_pos.view(3 ,1).contiguous()).view(3).contiguous() + parent_trans_vec
return self.pos
# initialize the robot with states set to zeros #
# update the robot via states #
# set a new action #
# update states via actions #
# update robot (visual points and parameters) via states #
## 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
#
self.radius = radius
self.visual_pts_ref = None
self.visual_faces_ref = None
self.visual_pts = None
self.body_name_to_main_axis = get_body_name_to_main_axis() ### get the body name to main axis here #
self.get_visual_counterparts()
# inertial_ref, inertial_ref_inv
def get_visual_faces_list(self, visual_faces_list):
visual_faces_list.append(self.visual_faces_ref)
return visual_faces_list
def compute_inertial_inv(self, rot_mtx):
cur_inertia_inv = torch.matmul(
rot_mtx, torch.matmul(self.inertial_ref_inv, rot_mtx.transpose(1, 0).contiguous()) ### passive obj rot transpose
)
self.cur_inertial_inv = cur_inertia_inv
return cur_inertia_inv
def compute_inertia(self, rot_mtx):
cur_inertia = torch.matmul(
rot_mtx, torch.matmul(self.inertial_ref, rot_mtx.transpose(1, 0))
)
self.cur_inertia = cur_inertia
return cur_inertia
def update_radius(self,):
self.radius.data = self.radius.data - self.radius.grad.data
self.radius.grad.data = self.radius.grad.data * 0.
### 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() 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
## optimize the action sequneces ##
def get_visual_counterparts(self,):
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"
body_mesh = trimesh.load(filename, process=False)
elif self.body_type == "mesh":
filename = self.filename
if "shadow" in xml_fn:
rt_asset_path = "/home/xueyi/diffsim/NeuS/rsc/shadow_hand_description"
else:
rt_asset_path = "/home/xueyi/diffsim/DiffHand/assets"
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)
elif self.body_type == "abstract":
body_mesh = trimesh.Trimesh(vertices=np.empty((0, 3), dtype=np.float32), faces=np.empty((0, 3), dtype=np.int32))
# body_mesh = trimesh.load(filename, process=False)
self.pos = nn.Parameter(
torch.tensor(self.pos.detach().cpu().tolist(), dtype=torch.float32, requires_grad=True).cuda(), 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(), requires_grad=True
# )
self.radius = nn.Parameter(
torch.tensor([2.], dtype=torch.float32, requires_grad=True).cuda(), requires_grad=True
)
### visual pts ref ### ## body_mesh.vertices -> #
self.visual_pts_ref = torch.tensor(body_mesh.vertices, dtype=torch.float32).cuda()
self.visual_faces_ref = torch.tensor(body_mesh.faces, dtype=torch.long).cuda()
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()
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.full_main_axis_pts_ref = full_main_axis_pts
else:
self.full_main_axis_pts_ref = mean_pts.clone()
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 transform_expanded_visual_pts(self, rot_mtx, trans_vec):
expanded_visual_pts_ref = self.expanded_visual_pts_ref
self.expanded_visual_pts = torch.matmul(rot_mtx, expanded_visual_pts_ref.transpose(1, 0)).transpose(1, 0) + trans_vec.unsqueeze(0)
return self.expanded_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.
def get_visual_pts(self, visual_pts_list):
visual_pts_list.append(self.visual_pts.detach())
return visual_pts_list
# 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
### dyn_model_act ###
# parent_rot_mtx, parent_trans_vec #
# parent_rot_mtx, parent_trans_vec #
self.parent_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(), requires_grad=True)
self.parent_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(), requires_grad=True)
self.curr_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(), requires_grad=True)
self.curr_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(), requires_grad=True)
#
self.tot_rot_mtx = nn.Parameter(torch.eye(n=3, dtype=torch.float32).cuda(), requires_grad=True)
self.tot_trans_vec = nn.Parameter(torch.zeros((3,), dtype=torch.float32).cuda(), requires_grad=True)
self.compute_inertia()
def print_grads(self, ):
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 compute_inertia(self, ):
joint_pos = self.joint.pos
joint_rot, joint_trans = self.joint.compute_transformation_from_current_state() # from current state #
body_pts = self.body.transform_visual_pts(joint_rot, joint_trans)
self.inertial_ref = torch.zeros((3, 3), dtype=torch.float32).cuda()
body_pts_mass = 1. / float(body_pts.size(0))
for i_pts in range(body_pts.size(0)):
cur_pts = body_pts[i_pts]
cur_pts_mass = body_pts_mass
cur_r = cur_pts - joint_pos
# cur_vert = init_passive_mesh[i_v] #
# cur_r = cur_vert - init_passive_mesh_center
dot_r_r = torch.sum(cur_r * cur_r)
cur_eye_mtx = torch.eye(3, dtype=torch.float32).cuda()
r_mult_rT = torch.matmul(cur_r.unsqueeze(-1), cur_r.unsqueeze(0))
self.inertial_ref += (dot_r_r * cur_eye_mtx - r_mult_rT) * cur_pts_mass
self.inertial_ref_inv = torch.linalg.inv(self.inertial_ref)
self.body.inertial_ref = self.inertial_ref.clone()
self.body.inertial_ref_inv = self.inertial_ref_inv.clone() ### inertial ref ###
# print(f"body_invertia_matrix_inv: {self.body.inertial_ref_inv}")
#
def set_init_states_target_value(self, init_states):
if self.joint.type == 'revolute':
self.joint_angle = init_states[self.joint.joint_idx]
joint_axis = self.joint.axis
self.rot_vec = self.joint_angle * joint_axis
self.joint.state = torch.tensor([1, 0, 0, 0], dtype=torch.float32).cuda()
self.joint.state = self.joint.state + update_quaternion(self.rot_vec, self.joint.state)
self.joint.timestep_to_states[0] = self.joint.state.detach()
self.joint.timestep_to_vels[0] = torch.zeros((3,), dtype=torch.float32).cuda().detach() ## velocity ##
for cur_link in self.children:
cur_link.set_init_states_target_value(init_states)
# should forward for one single step -> use the action #
def set_init_states(self, ):
self.joint.state = torch.tensor([1, 0, 0, 0], dtype=torch.float32).cuda()
self.joint.timestep_to_states[0] = self.joint.state.detach()
self.joint.timestep_to_vels[0] = torch.zeros((3,), dtype=torch.float32).cuda().detach() ## velocity ##
for cur_link in self.children:
cur_link.set_init_states()
def get_visual_pts(self, visual_pts_list):
visual_pts_list = self.body.get_visual_pts(visual_pts_list)
for cur_link in self.children:
visual_pts_list = cur_link.get_visual_pts(visual_pts_list)
visual_pts_list = torch.cat(visual_pts_list, dim=0)
return visual_pts_list
def get_visual_faces_list(self, visual_faces_list):
visual_faces_list = self.body.get_visual_faces_list(visual_faces_list)
for cur_link in self.children:
visual_faces_list = cur_link.get_visual_faces_list(visual_faces_list)
return visual_faces_list
# pass
# with link states # # stpe
def get_joint_states(self, joint_states):
if self.joint.type == 'revolute':
# joint_states.append(self.joint.state)
joint_idx = self.joint.joint_idx
def set_penetration_forces(self, penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces):
# penetration_forces
if self.children is not None and len(self.children) > 0:
for cur_link in self.children:
cur_link.set_penetration_forces(penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces)
if self.joint.type in ['revolute']:
# penetration_forces_values = penetration_forces['penetration_forces'] #
# penetration_forces_points = penetration_forces['penetration_forces_points'] #
# penetration forces #
penetration_forces_values = penetration_forces['penetration_forces'].detach()
penetration_forces_points = penetration_forces['penetration_forces_points'].detach()
####### use a part of peentration points and forces #######
if sampled_visual_pts_joint_idxes is not None:
selected_forces_mask = sampled_visual_pts_joint_idxes == self.joint.joint_idx
else:
selected_forces_mask = torch.ones_like(penetration_forces_values[:, 0]).bool()
####### use a part of peentration points and forces #######
####### use all peentration points and forces #######
# selected_forces_mask = torch.ones_like(penetration_forces_values[:, 0]).bool()
####### use all peentration points and forces #######
if torch.sum(selected_forces_mask.float()) > 0.5:
penetration_forces_values = penetration_forces_values[selected_forces_mask]
penetration_forces_points = penetration_forces_points[selected_forces_mask]
# tot_rot_mtx, tot_trans_vec
# cur_joint_rot = self.tot_rot_mtx
# cur_joint_trans = self.tot_trans_vec
cur_joint_rot = self.tot_rot_mtx.detach()
cur_joint_trans = self.tot_trans_vec.detach()
local_frame_penetration_forces_values = torch.matmul(cur_joint_rot.transpose(1, 0), penetration_forces_values.transpose(1, 0)).transpose(1, 0)
local_frame_penetration_forces_points = torch.matmul(cur_joint_rot.transpose(1, 0), (penetration_forces_points - cur_joint_trans.unsqueeze(0)).transpose(1, 0)).transpose(1, 0)
body_visual_pts_ref = self.body.visual_pts_ref
center_pts = torch.mean(body_visual_pts_ref, dim=0)
joint_pos_to_forces_points = local_frame_penetration_forces_points - center_pts.unsqueeze(0)
forces_torques = torch.cross(joint_pos_to_forces_points, local_frame_penetration_forces_values) # forces values of the local frame #
forces_torques = torch.sum(forces_torques, dim=0)
forces = torch.sum(local_frame_penetration_forces_values, dim=0)
cur_joint_maximal_forces = torch.cat(
[forces, forces_torques], dim=0
)
cur_joint_idx = self.joint.joint_idx
joint_penetration_forces[cur_joint_idx][:] = cur_joint_maximal_forces[:].clone()
# forces_torques_dot_axis = torch.sum(self.joint.axis * forces_torques)
# forces_torques = self.joint.axis * forces_torques_dot_axis
####### use children penetrations torqeus #######
# if children_penetration_torques is not None:
# children_penetration_torques_dot_axis = torch.sum(self.joint.axis * children_penetration_torques)
# children_penetration_torques = self.joint.axis * children_penetration_torques_dot_axis
# forces_torques = forces_torques + children_penetration_torques # * 0.5 # damping #
# children_penetration_torques = forces_torques.clone() * 0.5 # damping #
# else:
# children_penetration_torques = forces_torques.clone() * 0.5 #
####### use children penetrations torqeus #######
# force torques #
# torque = torque + forces_torques # * 0.001
# forward dynamics --- from actions to states #
# inertia matrix --- from the inertia matrix to the inertia matrix # # set actiosn and update states #
def set_actions_and_update_states(self, actions, cur_timestep, time_cons, penetration_forces=None, sampled_visual_pts_joint_idxes=None, joint_name_to_penetration_forces_intermediates=None, children_penetration_torques=None, buffered_intertia_matrix=None):
if self.children is not None and len(self.children) > 0:
# tot_children_intertia_matrix = torch.zeros((3, 3), dtype=torch.float32).cuda()
for cur_link in self.children:
tot_children_intertia_matrix = torch.zeros((3, 3), dtype=torch.float32).cuda()
cur_link.set_actions_and_update_states(actions, cur_timestep, time_cons, penetration_forces=penetration_forces, sampled_visual_pts_joint_idxes=sampled_visual_pts_joint_idxes, joint_name_to_penetration_forces_intermediates=joint_name_to_penetration_forces_intermediates, children_penetration_torques=children_penetration_torques, buffered_intertia_matrix=tot_children_intertia_matrix)
if buffered_intertia_matrix is not None:
buffered_intertia_matrix = buffered_intertia_matrix + tot_children_intertia_matrix
else:
buffered_intertia_matrix = tot_children_intertia_matrix
# tot_children_intertia_matri
# tot_children_intertia_matri = tot_children_intertia_matri + torch.eye(3, dtype=torch.float32).cuda() * 0.0001
# buffered_intertia_matrix
if self.joint.type in ['revolute']:
# return #
self.joint.action = actions[self.joint.joint_idx]
#
# visual_pts and visual_pts_mass
cur_joint_pos = self.joint.pos
# TODO: check whether the following is correct #
torque = self.joint.action * self.joint.axis ## joint.axis ##
# should along the axis #
# torques added to the joint #
# penetration forces -- a list of the forces ## penetration forces ###
if penetration_forces is not None:
# a series of the #
# penetration_forces: { 'global_rotation': xxx, 'global_translation': xxx, 'penetration_forces': xxx, 'penetration_forces_points': xxx }
# glb_rot = penetration_forces['global_rotation'] #
# # glb_trans = penetration_forces['global_translation'] #
# penetration_forces_values = penetration_forces['penetration_forces'] #
# penetration_forces_points = penetration_forces['penetration_forces_points'] #
# penetration forces # # values points #
penetration_forces_values = penetration_forces['penetration_forces'].detach()
penetration_forces_points = penetration_forces['penetration_forces_points'].detach()
####### use a part of peentration points and forces #######
if sampled_visual_pts_joint_idxes is not None:
selected_forces_mask = sampled_visual_pts_joint_idxes == self.joint.joint_idx
else:
selected_forces_mask = torch.ones_like(penetration_forces_values[:, 0]).bool()
####### use a part of peentration points and forces #######
####### use all peentration points and forces #######
selected_forces_mask = torch.ones_like(penetration_forces_values[:, 0]).bool()
####### use all peentration points and forces #######
if torch.sum(selected_forces_mask.float()) > 0.5:
penetration_forces_values = penetration_forces_values[selected_forces_mask]
penetration_forces_points = penetration_forces_points[selected_forces_mask]
# tot_rot_mtx, tot_trans_vec
# cur_joint_rot = self.tot_rot_mtx
# cur_joint_trans = self.tot_trans_vec
cur_joint_rot = self.tot_rot_mtx.detach()
cur_joint_trans = self.tot_trans_vec.detach()
local_frame_penetration_forces_values = torch.matmul(cur_joint_rot.transpose(1, 0), penetration_forces_values.transpose(1, 0)).transpose(1, 0)
local_frame_penetration_forces_points = torch.matmul(cur_joint_rot.transpose(1, 0), (penetration_forces_points - cur_joint_trans.unsqueeze(0)).transpose(1, 0)).transpose(1, 0)
joint_pos_to_forces_points = local_frame_penetration_forces_points - cur_joint_pos.unsqueeze(0)
forces_torques = torch.cross(joint_pos_to_forces_points, local_frame_penetration_forces_values) # forces values of the local frame #
forces_torques = torch.sum(forces_torques, dim=0)
forces_torques_dot_axis = torch.sum(self.joint.axis * forces_torques)
forces_torques = self.joint.axis * forces_torques_dot_axis
####### use children penetrations torqeus #######
# if children_penetration_torques is not None:
# children_penetration_torques_dot_axis = torch.sum(self.joint.axis * children_penetration_torques)
# children_penetration_torques = self.joint.axis * children_penetration_torques_dot_axis
# forces_torques = forces_torques + children_penetration_torques # * 0.5 # damping #
# children_penetration_torques = forces_torques.clone() * 0.5 # damping #
# else:
# children_penetration_torques = forces_torques.clone() * 0.5 #
####### use children penetrations torqeus #######
# force torques #
torque = torque + forces_torques # * 0.001
if joint_name_to_penetration_forces_intermediates is not None:
visual_pts = self.body.visual_pts_ref.detach().cpu().numpy()
forces_points_local_frame = local_frame_penetration_forces_points.detach().cpu().numpy()
forces_values_local_frame = local_frame_penetration_forces_values.detach().cpu().numpy()
joint_pos = cur_joint_pos.detach().cpu().numpy()
joint_axis = self.joint.axis.detach().cpu().numpy()
joint_name_to_penetration_forces_intermediates[self.joint.name] = {
'visual_pts': visual_pts, 'forces_points_local_frame': forces_points_local_frame, 'forces_values_local_frame': forces_values_local_frame, 'joint_pos': joint_pos, 'joint_axis': joint_axis
}
else:
if children_penetration_torques is not None:
children_penetration_torques = children_penetration_torques * 0.5
# # TODO: transform the forces to the joint frame #
# cur_penetration_torque = torch.zeros_like(torque)
# for cur_pene_force_set in penetration_forces:
# cur_pene_force, cur_pene_point = cur_pene_force_set
# joint_pos_to_pene_point = cur_pene_point - cur_joint_pos ## joint pos ##
# cur_point_pene_torque = torch.cross(joint_pos_to_pene_point, cur_pene_force)
# cur_penetration_torque += cur_point_pene_torque
# # # ##
# dot_axis_with_penetration_torque = torch.sum(self.joint.axis * cur_penetration_torque)
# cur_penetration_torque = self.joint.axis * dot_axis_with_penetration_torque
# torque = torque + cur_penetration_torque
# # Compute inertia matrix # #
# inertial = torch.zeros((3, 3), dtype=torch.float32).cuda()
# for i_pts in range(self.visual_pts.size(0)):
# cur_pts = self.visual_pts[i_pts]
# cur_pts_mass = self.visual_pts_mass[i_pts]
# cur_r = cur_pts - cur_joint_pos # r_i #
# # cur_vert = init_passive_mesh[i_v]
# # cur_r = cur_vert - init_passive_mesh_center
# dot_r_r = torch.sum(cur_r * cur_r)
# cur_eye_mtx = torch.eye(3, dtype=torch.float32).cuda()
# r_mult_rT = torch.matmul(cur_r.unsqueeze(-1), cur_r.unsqueeze(0))
# inertial += (dot_r_r * cur_eye_mtx - r_mult_rT) * cur_pts_mass
# m = torch.sum(self.visual_pts_mass)
# # Use torque to update angular velocity -> state #
# inertia_inv = torch.linalg.inv(inertial)
# axis-angle of # axis-angle # # a) joint torque; # b) external force and torque #
# potision of the force # # link a; body a # body to the joint # # body to the joint # #
# force applied to the joint torque # # torque #
# change the angles #
# inertia_inv = self.cur_inertia_inv
inertia_inv = torch.linalg.inv(self.cur_inertia).detach()
inertia_inv = torch.eye(n=3, dtype=torch.float32).cuda()
if buffered_intertia_matrix is not None:
buffered_intertia_matrix = buffered_intertia_matrix + torch.eye(n=3, dtype=torch.float32).cuda()
else:
buffered_intertia_matrix = torch.eye(n=3, dtype=torch.float32).cuda()
inertia_inv = torch.linalg.inv(buffered_intertia_matrix).detach()
delta_omega = torch.matmul(inertia_inv, torque.unsqueeze(-1)).squeeze(-1)
#
# delta_omega = torque / 400 # # apply the force onto the link; apply the force onto the link
# TODO: dt should be an optimizable constant? should it be the same value as that optimized for the passive object? #
delta_angular_vel = delta_omega * time_cons # * self.args.dt # delta quat #
delta_angular_vel = delta_angular_vel.squeeze(0)
if cur_timestep > 0:
prev_angular_vel = self.joint.timestep_to_vels[cur_timestep - 1].detach()
cur_angular_vel = prev_angular_vel * DAMPING + delta_angular_vel
else:
cur_angular_vel = delta_angular_vel # delta
self.joint.timestep_to_vels[cur_timestep] = cur_angular_vel.detach()
# TODO: about args.dt
cur_delta_quat = cur_angular_vel * time_cons
cur_delta_quat = cur_delta_quat.squeeze(0) # delta quat #
cur_state = self.joint.timestep_to_states[cur_timestep].detach()
nex_state = cur_state + update_quaternion(cur_delta_quat, cur_state)
self.joint.timestep_to_states[cur_timestep + 1] = nex_state.detach() #
self.joint.state = nex_state
# followed by updating visual pts using states # #
# print(f"updated_joint_state: {self.joint.state}")
## for the robot: iterate over links and get the states ##
def get_joint_nm_to_states(self, joint_nm_to_states):
if self.joint.type in ['revolute']:
joint_nm_to_states[self.joint.name] = self.joint.state
if self.children is not None and len(self.children) > 0:
for cur_link in self.children:
joint_nm_to_states = cur_link.get_joint_nm_to_states(joint_nm_to_states)
return joint_nm_to_states
def get_timestep_to_states(self, joint_nm_to_ts_to_states):
if self.joint.type in ['revolute']:
joint_nm_to_ts_to_states[self.joint.name] = self.joint.timestep_to_states
if self.children is not None and len(self.children) > 0:
for cur_link in self.children:
joint_nm_to_ts_to_states = cur_link.get_timestep_to_states(joint_nm_to_ts_to_states)
return joint_nm_to_ts_to_states
# current delta states # get states -- reference states # joint
def set_and_update_states(self, states, cur_timestep, time_cons):
#
if self.joint.type in ['revolute']:
# return # # prev
cur_state = states[self.joint.joint_idx] # joint idx #
#
# self.joint.timestep_to_states[cur_timestep + 1] = cur_state.detach()
delta_rot_vec = self.joint.axis * cur_state # states -->
prev_state = self.joint.timestep_to_states[cur_timestep].detach()
cur_state = prev_state + update_quaternion(delta_rot_vec, prev_state)
self.joint.timestep_to_states[cur_timestep + 1] = cur_state.detach()
self.joint.state = cur_state
# followed by updating visual pts using states #
# print(f"updated_joint_state: {self.joint.state}")
# link and the states #
if self.children is not None and len(self.children) > 0:
for cur_link in self.children: # glb trans #
cur_link.set_and_update_states(states, cur_timestep, time_cons)
#
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)
##
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 ###
# current rot #
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 compute_transformation_via_current_state(self, parent_rot_mtx, parent_trans_vec, visual_pts_list, visual_pts_mass, link_name_to_transformations_and_transformed_pts, joint_idxes=None):
# state vecs and rot mtx # state vecs #
joint_rot_mtx, joint_trans_vec = self.joint.compute_transformation_from_current_state()
# cur_inertia_inv
self.cur_inertia_inv = torch.zeros((3, 3), dtype=torch.float32).cuda()
self.cur_inertia = torch.zeros((3, 3), dtype=torch.float32).cuda()
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 ###
# get the parent rot mtx and the joint rot mtx # # joint rot mtx #
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
# tot_rot_mtx, tot_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 ### # !!!! damping is an important technique here ! ####
### visual pts list for recoding visual pts ###
# so the inertial should be transformed by the tot_rot_mtx # # transform visual pts # # tr
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)
self.cur_inertia_inv = self.cur_inertia_inv + self.body.compute_inertial_inv(self.tot_rot_mtx)
self.cur_inertia = self.cur_inertia + self.body.compute_inertia(self.tot_rot_mtx)
#
cur_body_transformations = (self.tot_rot_mtx, self.tot_trans_vec)
link_name_to_transformations_and_transformed_pts[self.body.name] = (cur_body_visual_pts.detach().clone(), cur_body_transformations)
if joint_idxes is not None:
cur_body_joint_idx = self.joint.joint_idx
cur_body_joint_idxes = [cur_body_joint_idx for _ in range(cur_body_visual_pts.size(0))]
cur_body_joint_idxes = torch.tensor(cur_body_joint_idxes, dtype=torch.long).cuda()
joint_idxes.append(cur_body_joint_idxes)
#
children_visual_pts_list = []
children_pts_mass = []
children_pts_mass.append(torch.ones((cur_body_visual_pts.size(0), ), dtype=torch.float32).cuda() / float(cur_body_visual_pts.size(0)))
children_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
children_visual_pts_list, children_pts_mass = cur_link.compute_transformation_via_current_state(tot_parent_rot_mtx, tot_parent_trans_vec, children_visual_pts_list, children_pts_mass, link_name_to_transformations_and_transformed_pts, joint_idxes=joint_idxes)
## inertia_inv ##
self.cur_inertia_inv = self.cur_inertia_inv + cur_link.cur_inertia_inv
self.cur_inertia = self.cur_inertia + cur_link.cur_inertia ### get the current inertia ###
children_visual_pts = torch.cat(children_visual_pts_list, dim=0)
self.visual_pts = children_visual_pts.detach() #
visual_pts_list.append(children_visual_pts)
children_pts_mass = torch.cat(children_pts_mass, dim=0)
self.visual_pts_mass = children_pts_mass.detach()
visual_pts_mass.append(children_pts_mass)
# print(f"children_pts_mass: {children_pts_mass.size()}")
return visual_pts_list, visual_pts_mass
def compute_expanded_visual_pts_transformation_via_current_state(self, parent_rot_mtx, parent_trans_vec, visual_pts_list, visual_pts_mass):
# state vecs and rot mtx # state vecs ##### #
joint_rot_mtx, joint_trans_vec = self.joint.compute_transformation_from_current_state()
# cur_inertia_inv
self.cur_inertia_inv = torch.zeros((3, 3), dtype=torch.float32).cuda()
self.cur_inertia = torch.zeros((3, 3), dtype=torch.float32).cuda()
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 ###
# get the parent rot mtx and the joint rot mtx # # joint rot mtx #
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 ###
# so the inertial should be transformed by the tot_rot_mtx # # transform visual pts #
cur_body_visual_pts = self.body.transform_expanded_visual_pts(rot_mtx=self.tot_rot_mtx, trans_vec=self.tot_trans_vec)
# visual_pts_list.append(cur_body_visual_pts)
self.cur_inertia_inv = self.cur_inertia_inv + self.body.compute_inertial_inv(self.tot_rot_mtx)
self.cur_inertia = self.cur_inertia + self.body.compute_inertia(self.tot_rot_mtx)
#
# cur_body_transformations = (self.tot_rot_mtx.detach().clone(), self.tot_trans_vec.detach().clone())
# link_name_to_transformations_and_transformed_pts[self.body.name] = (cur_body_visual_pts.detach().clone(), cur_body_transformations)
#
children_visual_pts_list = []
children_pts_mass = []
children_pts_mass.append(torch.ones((cur_body_visual_pts.size(0), ), dtype=torch.float32).cuda() / float(cur_body_visual_pts.size(0)))
children_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
children_visual_pts_list, children_pts_mass = cur_link.compute_expanded_visual_pts_transformation_via_current_state(tot_parent_rot_mtx, tot_parent_trans_vec, children_visual_pts_list, children_pts_mass)
self.cur_inertia_inv = self.cur_inertia_inv + cur_link.cur_inertia_inv
self.cur_inertia = self.cur_inertia + cur_link.cur_inertia ### get the current inertia ###
children_visual_pts = torch.cat(children_visual_pts_list, dim=0)
self.expanded_visual_pts = children_visual_pts.detach() #
visual_pts_list.append(children_visual_pts)
children_pts_mass = torch.cat(children_pts_mass, dim=0)
self.expanded_visual_pts_mass = children_pts_mass.detach()
visual_pts_mass.append(children_pts_mass)
# print(f"children_pts_mass: {children_pts_mass.size()}")
return visual_pts_list, visual_pts_mass
def set_body_expanded_visual_pts(self, link_name_to_ragged_expanded_visual_pts):
self.body.expanded_visual_pts_ref = link_name_to_ragged_expanded_visual_pts[self.body.name].detach().clone()
for cur_link in self.children:
cur_link.set_body_expanded_visual_pts(link_name_to_ragged_expanded_visual_pts)
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
visual_pts_list = cur_link.get_visual_pts_list(visual_pts_list)
return 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.
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
### global rotation quaternion ###
self.glb_rotation = nn.Parameter(torch.eye(3, dtype=torch.float32, requires_grad=True).cuda(), requires_grad=True)
### global translation vectors ##
self.glb_trans = nn.Parameter(torch.tensor([ 0., 0., 0.], dtype=torch.float32, requires_grad=True).cuda(), requires_grad=True)
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:
visual_pts_list = cur_link.get_visual_pts_list(visual_pts_list)
return visual_pts_list
def get_visual_faces_list(self, visual_faces_list):
for cur_link in self.children:
visual_faces_list = cur_link.get_visual_faces_list(visual_faces_list)
return visual_faces_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): # i
for cur_link in self.children: #
transformed_joints = cur_link.get_tot_transformed_joints(transformed_joints)
return transformed_joints
def get_joint_states(self, joint_states):
for cur_link in self.children:
joint_states = cur_link.get_joint_states(joint_states)
return joint_states
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):
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 set_init_states(self, init_states):
# glb_rot, glb_trans #
###### set the initial state ######
glb_rot = init_states['glb_rot']
self.glb_rotation.data[:, :] = glb_rot[:, :]
glb_trans = init_states['glb_trans']
self.glb_trans.data[:] = glb_trans[:] # glb trans #
# parent_rot_mtx, parent_trans_vec #
for cur_link in self.children:
cur_link.parent_rot_mtx.data[:, :] = self.glb_rotation.data[:, :]
cur_link.parent_trans_vec.data[:] = self.glb_trans.data[:]
cur_link.set_init_states()
def set_init_states_target_value(self, tot_init_states):
glb_rot = tot_init_states['glb_rot']
self.glb_rotation.data[:, :] = glb_rot[:, :]
glb_trans = tot_init_states['glb_trans']
self.glb_trans.data[:] = glb_trans[:] # glb trans #
links_init_states = tot_init_states['links_init_states']
for cur_link in self.children:
cur_link.parent_rot_mtx.data[:, :] = self.glb_rotation.data[:, :]
cur_link.parent_trans_vec.data[:] = self.glb_trans.data[:]
cur_link.set_init_states_target_value(links_init_states)
def get_timestep_to_states(self, joint_nm_to_ts_to_states):
for cur_link in self.children:
joint_nm_to_ts_to_states = cur_link.get_timestep_to_states(joint_nm_to_ts_to_states)
return joint_nm_to_ts_to_states
def get_joint_nm_to_states(self, joint_nm_to_states):
for cur_link in self.children:
joint_nm_to_states = cur_link.get_joint_nm_to_states(joint_nm_to_states)
return joint_nm_to_states
# def set_penetration_forces(self, penetration_forces):
def set_penetration_forces(self, penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces):
for cur_link in self.children:
cur_link.set_penetration_forces(penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces)
# set_actions_and_update_states(..., penetration_forces)
def set_actions_and_update_states(self, actions, cur_timestep, time_cons, penetration_forces=None, sampled_visual_pts_joint_idxes=None, joint_name_to_penetration_forces_intermediates=None):
# delta_glb_rot; delta_glb_trans # #
delta_glb_rotation = actions['delta_glb_rot']
delta_glb_trans = actions['delta_glb_trans']
cur_glb_rot = self.glb_rotation.data.detach()
cur_glb_trans = self.glb_trans.data.detach()
nex_glb_rot = torch.matmul(delta_glb_rotation, cur_glb_rot) #
nex_glb_trans = torch.matmul(delta_glb_rotation, cur_glb_trans.unsqueeze(-1)).squeeze(-1) + delta_glb_trans
link_actions = actions['link_actions']
self.glb_rotation = nex_glb_rot
self.glb_trans = nex_glb_trans
for cur_link in self.children: # glb trans # #
cur_link.set_actions_and_update_states(link_actions, cur_timestep, time_cons, penetration_forces=penetration_forces, sampled_visual_pts_joint_idxes=sampled_visual_pts_joint_idxes, joint_name_to_penetration_forces_intermediates=joint_name_to_penetration_forces_intermediates, children_penetration_torques=None)
def set_and_update_states(self, states, cur_timestep, time_cons):
delta_glb_rotation = states['delta_glb_rot'] #
delta_glb_trans = states['delta_glb_trans']
cur_glb_rot = self.glb_rotation.data.detach()
cur_glb_trans = self.glb_trans.data.detach()
nex_glb_rot = torch.matmul(delta_glb_rotation, cur_glb_rot)
nex_glb_trans = torch.matmul(delta_glb_rotation, cur_glb_trans.unsqueeze(-1)).squeeze(-1) + delta_glb_trans
link_states = states['link_states']
self.glb_rotation = nex_glb_rot
self.glb_trans = nex_glb_trans
for cur_link in self.children: # glb trans #
cur_link.set_and_update_states(link_states, cur_timestep, time_cons)
def compute_transformation_via_current_state(self, visual_pts_list, link_name_to_transformations_and_transformed_pts, joint_idxes= None):
# visual_pts_mass_list = []
visual_pts_list = []
visual_pts_mass_list = []
# visual_pts_mass = []
for cur_link in self.children:
visual_pts_list, visual_pts_mass_list = cur_link.compute_transformation_via_current_state(self.glb_rotation.data, self.glb_trans.data, visual_pts_list, visual_pts_mass_list, link_name_to_transformations_and_transformed_pts, joint_idxes=joint_idxes)
visual_pts_list = torch.cat(visual_pts_list, dim=0)
visual_pts_mass_list= torch.cat(visual_pts_mass_list, dim=0)
return visual_pts_list, visual_pts_mass_list
# compute_expanded_visual_pts_transformation_via_current_state
def compute_expanded_visual_pts_transformation_via_current_state(self, visual_pts_list):
# visual_pts_mass_list = []
visual_pts_list = []
visual_pts_mass_list = []
# visual_pts_mass = []
for cur_link in self.children:
visual_pts_list, visual_pts_mass_list = cur_link.compute_expanded_visual_pts_transformation_via_current_state(self.glb_rotation.data, self.glb_trans.data, visual_pts_list, visual_pts_mass_list)
visual_pts_list = torch.cat(visual_pts_list, dim=0)
visual_pts_mass_list= torch.cat(visual_pts_mass_list, dim=0)
return visual_pts_list, visual_pts_mass_list
def set_body_expanded_visual_pts(self, link_name_to_ragged_expanded_visual_pts):
for cur_link in self.children:
cur_link.set_body_expanded_visual_pts(link_name_to_ragged_expanded_visual_pts)
# robot manager #
# set the initial state #
# record optimizable actions #
# record optimizable time constants #
# and with the external forces? #
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(), 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 merge_meshes(verts_list, faces_list):
nn_verts = 0
tot_verts_list = []
tot_faces_list = []
for i_vv, cur_verts in enumerate(verts_list):
cur_verts_nn = cur_verts.size(0)
tot_verts_list.append(cur_verts)
tot_faces_list.append(faces_list[i_vv] + nn_verts)
nn_verts = nn_verts + cur_verts_nn
tot_verts_list = torch.cat(tot_verts_list, dim=0)
tot_faces_list = torch.cat(tot_faces_list, dim=0)
return tot_verts_list, tot_faces_list
class RobotAgent: # robot and the robot #
def __init__(self, xml_fn, args) -> None:
self.xml_fn = xml_fn
self.args = args
##
active_robot, passive_robot = parse_data_from_xml(xml_fn, args)
#### set and initialize the time constant ####
self.time_constant = nn.Embedding(
num_embeddings=3, embedding_dim=1
).cuda()
torch.nn.init.ones_(self.time_constant.weight) #
self.time_constant.weight.data = self.time_constant.weight.data * 0.2 ### time_constant data #
#### set optimizable actions ####
self.optimizable_actions = nn.Embedding(
num_embeddings=100, embedding_dim=22,
).cuda()
torch.nn.init.zeros_(self.optimizable_actions.weight) #
self.learning_rate = 5e-4
self.active_robot = active_robot
self.set_init_states()
init_visual_pts = self.get_init_state_visual_pts()
self.init_visual_pts = init_visual_pts
self.robot_visual_faces_list = []
self.robot_visual_faces_list = self.active_robot.get_visual_faces_list(self.robot_visual_faces_list)
self.robot_visual_pts_list = []
self.robot_visual_pts_list = self.active_robot.get_visual_pts_list(self.robot_visual_pts_list)
self.robot_pts, self.robot_faces = merge_meshes(self.robot_visual_pts_list, self.robot_visual_faces_list)
print(f"robot_pts: {self.robot_pts.size()}, self.robot_faces: {self.robot_faces.size()}")
cur_robot_mesh = trimesh.Trimesh(vertices=self.robot_pts.detach().cpu().numpy(), faces=self.robot_faces.detach().cpu().numpy())
cur_robot_mesh.export(f'init_robot_mesh.ply')
def get_timestep_to_states(self):
joint_nm_to_ts_to_states = {}
joint_nm_to_ts_to_states = self.active_robot.get_timestep_to_states(joint_nm_to_ts_to_states)
return joint_nm_to_ts_to_states
# so for each joint; get the joint
def get_joint_nm_to_states(self):
joint_nm_to_states = {}
joint_nm_to_states = self.active_robot.get_joint_nm_to_states(joint_nm_to_states)
return joint_nm_to_states
def set_init_states_target_value(self, init_states):
glb_rot = torch.eye(n=3, dtype=torch.float32).cuda()
glb_trans = torch.zeros((3,), dtype=torch.float32).cuda() ### glb_trans #### and the rot 3##
tot_init_states = {}
tot_init_states['glb_rot'] = glb_rot;
tot_init_states['glb_trans'] = glb_trans;
tot_init_states['links_init_states'] = init_states
self.active_robot.set_init_states_target_value(tot_init_states)
def set_penetration_forces(self, penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces):
self.active_robot.set_penetration_forces(penetration_forces, sampled_visual_pts_joint_idxes, joint_penetration_forces)
def set_init_states(self):
glb_rot = torch.eye(n=3, dtype=torch.float32).cuda()
glb_trans = torch.zeros((3,), dtype=torch.float32).cuda() ### glb_trans #### and the rot 3##
### random rotation ###
# glb_rot_np = R.random().as_matrix()
# glb_rot = torch.from_numpy(glb_rot_np).float().cuda()
### random rotation ###
# glb_rot, glb_trans #
init_states = {}
init_states['glb_rot'] = glb_rot;
init_states['glb_trans'] = glb_trans;
self.active_robot.set_init_states(init_states)
def get_init_state_visual_pts(self, ret_link_name_to_tansformations=False, ret_joint_idxes=False):
visual_pts_list = [] # compute the transformation via current state #
link_name_to_transformations_and_transformed_pts = {}
joint_idxes = []
visual_pts_list, visual_pts_mass_list = self.active_robot.compute_transformation_via_current_state( visual_pts_list, link_name_to_transformations_and_transformed_pts, joint_idxes=joint_idxes)
joint_idxes = torch.cat(joint_idxes, dim=0)
init_visual_pts = visual_pts_list
if ret_link_name_to_tansformations and ret_joint_idxes:
return init_visual_pts, link_name_to_transformations_and_transformed_pts, joint_idxes
elif ret_link_name_to_tansformations:
return init_visual_pts, link_name_to_transformations_and_transformed_pts
elif ret_joint_idxes:
return init_visual_pts, joint_idxes
else:
return init_visual_pts
# init_visual_pts, link_name_to_transformations_and_transformed_pts = get_init_state_visual_pts(ret_link_name_to_tansformations=True)
# set_body_expanded_visual_pts
# expanded_visual_pts = compute_expanded_visual_pts_transformation_via_current_state()
# expanded_init_visual_pts = compute_expanded_visual_pts_transformation_via_current_state
def compute_expanded_visual_pts_transformation_via_current_state(self,):
visual_pts_list = [] # compute the transformation via current state #
# link_name_to_transformations_and_transformed_pts = {}
visual_pts_list, visual_pts_mass_list = self.active_robot.compute_expanded_visual_pts_transformation_via_current_state( visual_pts_list)
# init_visual_pts = visual_pts_list
# if ret_link_name_to_tansformations:
# return init_visual_pts, link_name_to_transformations_and_transformed_pts
# else:
return visual_pts_list
def set_body_expanded_visual_pts(self, link_name_to_ragged_expanded_visual_pts):
self.active_robot.set_body_expanded_visual_pts(link_name_to_ragged_expanded_visual_pts)
# for cur_link in self.children:
# cur_link.set_body_expanded_visual_pts(link_name_to_ragged_expanded_visual_pts)
def set_and_update_states(self, states, cur_timestep):
time_cons = self.time_constant(torch.zeros((1,), dtype=torch.long).cuda()) #
## set and update the states ##
self.active_robot.set_and_update_states(states, cur_timestep, time_cons)
# for cur_link in self.children: # glb trans #
# cur_link.set_and_update_states(link_actions, cur_timestep, time_cons)
# set_actions_and_update_states(..., penetration_forces)
def set_actions_and_update_states(self, actions, cur_timestep, penetration_forces=None, sampled_visual_pts_joint_idxes=None):
#
joint_name_to_penetration_forces_intermediates = {}
time_cons = self.time_constant(torch.zeros((1,), dtype=torch.long).cuda()) ### time constant of the system ##
self.active_robot.set_actions_and_update_states(actions, cur_timestep, time_cons, penetration_forces=penetration_forces, sampled_visual_pts_joint_idxes=sampled_visual_pts_joint_idxes, joint_name_to_penetration_forces_intermediates=joint_name_to_penetration_forces_intermediates) ###
return joint_name_to_penetration_forces_intermediates
def forward_stepping_test(self, ):
# delta_glb_rot; delta_glb_trans #
timestep_to_visual_pts = {}
for i_step in range(50):
actions = {}
actions['delta_glb_rot'] = torch.eye(3, dtype=torch.float32).cuda()
actions['delta_glb_trans'] = torch.zeros((3,), dtype=torch.float32).cuda()
actions_link_actions = torch.ones((22, ), dtype=torch.float32).cuda()
# actions_link_actions = actions_link_actions * 0.2
actions_link_actions = actions_link_actions * -1. #
actions['link_actions'] = actions_link_actions
self.set_actions_and_update_states(actions=actions, cur_timestep=i_step)
cur_visual_pts = robot_agent.get_init_state_visual_pts()
cur_visual_pts = cur_visual_pts.detach().cpu().numpy()
timestep_to_visual_pts[i_step + 1] = cur_visual_pts
return timestep_to_visual_pts
def initialize_optimization(self, reference_pts_dict):
self.n_timesteps = 50
self.n_timesteps = 19 # first 19-timesteps optimization #
self.nn_tot_optimization_iters = 1000
# self.nn_tot_optimization_iters = 57
# TODO: load reference points #
self.ts_to_reference_pts = np.load(reference_pts_dict, allow_pickle=True).item() ####
self.ts_to_reference_pts = {
ts: torch.from_numpy(self.ts_to_reference_pts[ts]).float().cuda() for ts in self.ts_to_reference_pts
}
# optimize the glboal state and
def forward_stepping_optimization(self, ):
nn_tot_optimization_iters = self.nn_tot_optimization_iters
params_to_train = []
params_to_train += list(self.optimizable_actions.parameters())
self.optimizer = torch.optim.Adam(params_to_train, lr=self.learning_rate)
for i_iter in range(nn_tot_optimization_iters):
tot_losses = []
ts_to_robot_points = {}
for cur_ts in range(self.n_timesteps):
# print(f"iter: {i_iter}, cur_ts: {cur_ts}")
actions = {}
actions['delta_glb_rot'] = torch.eye(3, dtype=torch.float32).cuda()
actions['delta_glb_trans'] = torch.zeros((3,), dtype=torch.float32).cuda()
actions_link_actions = self.optimizable_actions(torch.zeros((1,), dtype=torch.long).cuda() + cur_ts).squeeze(0)
# actions_link_actions = actions_link_actions * 0.2
# actions_link_actions = actions_link_actions * -1. #
actions['link_actions'] = actions_link_actions
self.set_actions_and_update_states(actions=actions, cur_timestep=cur_ts) # update the interaction #
cur_visual_pts = robot_agent.get_init_state_visual_pts()
ts_to_robot_points[cur_ts + 1] = cur_visual_pts.clone()
cur_reference_pts = self.ts_to_reference_pts[cur_ts + 1]
diff = torch.sum((cur_visual_pts - cur_reference_pts) ** 2, dim=-1)
diff = diff.mean()
# diff.
self.optimizer.zero_grad()
diff.backward()
self.optimizer.step()
tot_losses.append(diff.item())
# for ts in ts_to_robot_points:
# # print(f"ts: {ts}")
# if not ts in self.ts_to_reference_pts:
# continue
# cur_robot_pts = ts_to_robot_points[ts]
# cur_reference_pts = self.ts_to_reference_pts[ts]
# diff = torch.sum((cur_robot_pts - cur_reference_pts) ** 2, dim=-1)
# diff = torch.mean(diff)
# tot_losses.append(diff)
loss = sum(tot_losses) / float(len(tot_losses))
print(f"Iter: {i_iter}, average loss: {loss}")
# print(f"Iter: {i_iter}, average loss: {loss.item()}, start optimizing")
# self.optimizer.zero_grad()
# loss.backward()
# self.optimizer.step()
self.ts_to_robot_points = {
ts: ts_to_robot_points[ts].detach().cpu().numpy() for ts in ts_to_robot_points
}
self.ts_to_ref_points = {
ts: self.ts_to_reference_pts[ts].detach().cpu().numpy() for ts in ts_to_robot_points
}
return self.ts_to_robot_points, self.ts_to_ref_points
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
#### Big TODO: the external contact forces from the manipulated object to the robot ####
## optimize for actions from the redmax model ##
if __name__=='__main__': # agent of the
xml_fn = "/home/xueyi/diffsim/DiffHand/assets/hand_sphere.xml"
robot_agent = RobotAgent(xml_fn=xml_fn, args=None)
init_visual_pts = robot_agent.init_visual_pts.detach().cpu().numpy()
exit(0)
xml_fn = "/home/xueyi/diffsim/NeuS/rsc/shadow_hand_description/shadowhand_new_scaled_nroot.xml"
robot_agent = RobotAgent(xml_fn=xml_fn, args=None)
init_visual_pts = robot_agent.init_visual_pts.detach().cpu().numpy()
robot_agent.forward_stepping_test()
cur_visual_pts = robot_agent.get_init_state_visual_pts()
cur_visual_pts = cur_visual_pts.detach().cpu().numpy()
reference_pts_dict = "timestep_to_visual_pts.npy"
robot_agent.initialize_optimization(reference_pts_dict=reference_pts_dict)
optimized_ts_to_visual_pts, ts_to_ref_points = robot_agent.forward_stepping_optimization()
timestep_to_visual_pts = robot_agent.forward_stepping_test()
np.save(f"cur_visual_pts.npy", timestep_to_visual_pts) # cur_visual_pts #
np.save(f"timestep_to_visual_pts_opt.npy", timestep_to_visual_pts)
np.save(f"timestep_to_visual_pts_opt.npy", optimized_ts_to_visual_pts)
np.save(f"timestep_to_ref_pts.npy", ts_to_ref_points)
exit(0)
xml_fn = "/home/xueyi/diffsim/DiffHand/assets/hand_sphere.xml"
robot_agent = RobotAgent(xml_fn=xml_fn, args=None)
init_visual_pts = robot_agent.init_visual_pts.detach().cpu().numpy()
# np.save(f"init_visual_pts.npy", init_visual_pts) #
# robot_agent.forward_stepping_test()
# cur_visual_pts = robot_agent.get_init_state_visual_pts()
# cur_visual_pts = cur_visual_pts.detach().cpu().numpy()
# reference_pts_dict = "timestep_to_visual_pts.npy"
# robot_agent.initialize_optimization(reference_pts_dict=reference_pts_dict)
# optimized_ts_to_visual_pts, ts_to_ref_points = robot_agent.forward_stepping_optimization()
# timestep_to_visual_pts = robot_agent.forward_stepping_test()
# np.save(f"cur_visual_pts.npy", timestep_to_visual_pts) # cur_visual_pts #
# np.save(f"timestep_to_visual_pts_opt.npy", timestep_to_visual_pts)
# np.save(f"timestep_to_visual_pts_opt.npy", optimized_ts_to_visual_pts)
# np.save(f"timestep_to_ref_pts.npy", ts_to_ref_points)
exit(0)
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}")