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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
from torch.distributions.uniform import Uniform
# deformable articulated objects with the articulated models #
DAMPING = 1.0
DAMPING = 0.3
def plane_rotation_matrix_from_angle_xz(angle):
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 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))
class Inertial:
def __init__(self, origin_rpy, origin_xyz, mass, inertia) -> None:
self.origin_rpy = origin_rpy
self.origin_xyz = origin_xyz
self.mass = mass
self.inertia = inertia
if torch.sum(self.inertia).item() < 1e-4:
self.inertia = self.inertia + torch.eye(3, dtype=torch.float32).cuda()
pass
class Visual:
def __init__(self, visual_xyz, visual_rpy, geometry_mesh_fn, geometry_mesh_scale) -> None:
# self.visual_origin = visual_origin
self.visual_xyz = visual_xyz
self.visual_rpy = visual_rpy
self.mesh_nm = geometry_mesh_fn.split("/")[-1].split(".")[0]
mesh_root = "/home/xueyi/diffsim/NeuS/rsc/mano" ## mano models of the mesh root ##
if not os.path.exists(mesh_root):
mesh_root = "/data/xueyi/diffsim/NeuS/rsc/mano"
self.mesh_root = mesh_root
self.geometry_mesh_fn = os.path.join(mesh_root, geometry_mesh_fn)
self.geometry_mesh_scale = geometry_mesh_scale
# tranformed by xyz #
self.vertices, self.faces = self.load_geoemtry_mesh()
self.cur_expanded_visual_pts = None
pass
def load_geoemtry_mesh(self, ):
# mesh_root =
mesh = trimesh.load_mesh(self.geometry_mesh_fn)
vertices = mesh.vertices
faces = mesh.faces
vertices = torch.from_numpy(vertices).float().cuda()
faces =torch.from_numpy(faces).long().cuda()
vertices = vertices * self.geometry_mesh_scale.unsqueeze(0) + self.visual_xyz.unsqueeze(0)
return vertices, faces
# init_visual_meshes = get_init_visual_meshes(self, parent_rot, parent_trans, init_visual_meshes)
def get_init_visual_meshes(self, parent_rot, parent_trans, init_visual_meshes):
# cur_vertices = torch.matmul(parent_rot, self.vertices.transpose(1, 0)).contiguous().transpose(1, 0).contiguous() + parent_trans.unsqueeze(0)
cur_vertices = self.vertices
# print(f"adding mesh loaded from {self.geometry_mesh_fn}")
init_visual_meshes['vertices'].append(cur_vertices) # cur vertices # trans #
init_visual_meshes['faces'].append(self.faces)
return init_visual_meshes
def expand_visual_pts(self, ):
expand_factor = 0.2
nn_expand_pts = 20
expand_factor = 0.4
nn_expand_pts = 40 ### number of the expanded points ### ## points ##
expand_save_fn = f"{self.mesh_nm}_expanded_pts_factor_{expand_factor}_nnexp_{nn_expand_pts}.npy"
expand_save_fn = os.path.join(self.mesh_root, expand_save_fn)
if not os.path.exists(expand_save_fn):
cur_expanded_visual_pts = []
if self.cur_expanded_visual_pts is None:
cur_src_pts = self.vertices
else:
cur_src_pts = self.cur_expanded_visual_pts
maxx_verts, _ = torch.max(cur_src_pts, dim=0)
minn_verts, _ = torch.min(cur_src_pts, dim=0)
extent_verts = maxx_verts - minn_verts ## (3,)-dim vecotr
norm_extent_verts = torch.norm(extent_verts, dim=-1).item() ## (1,)-dim vector
expand_r = norm_extent_verts * expand_factor
# nn_expand_pts = 5 # expand the vertices to 5 times of the original vertices
for i_pts in range(self.vertices.size(0)):
cur_pts = cur_src_pts[i_pts]
# sample from the circile with cur_pts as thejcenter and the radius as expand_r
# (-r, r) # sample the offset vector in the size of (nn_expand_pts, 3)
offset_dist = Uniform(-1. * expand_r, expand_r)
offset_vec = offset_dist.sample((nn_expand_pts, 3)).cuda()
cur_expanded_pts = cur_pts + offset_vec
cur_expanded_visual_pts.append(cur_expanded_pts)
cur_expanded_visual_pts = torch.cat(cur_expanded_visual_pts, dim=0)
np.save(expand_save_fn, cur_expanded_visual_pts.detach().cpu().numpy())
else:
print(f"Loading visual pts from {expand_save_fn}") # load from the fn #
cur_expanded_visual_pts = np.load(expand_save_fn, allow_pickle=True)
cur_expanded_visual_pts = torch.from_numpy(cur_expanded_visual_pts).float().cuda()
self.cur_expanded_visual_pts = cur_expanded_visual_pts # expanded visual pts #
return self.cur_expanded_visual_pts
# cur_pts #
# use r as the search direction # # expande save fn #
def get_transformed_visual_pts(self, visual_pts_list):
visual_pts_list.append(self.cur_expanded_visual_pts) #
return visual_pts_list
## link urdf ## expand the visual pts to form the expanded visual grids pts #
# use get_name_to_visual_pts_faces to get the transformed visual pts and faces #
## Link_urdf ##
class Link_urdf: # get_transformed_visual_pts #
def __init__(self, name, inertial: Inertial, visual: Visual=None) -> None:
self.name = name
self.inertial = inertial
self.visual = visual # vsiual meshes #
# self.joint = joint
# self.body = body
# self.children = children
# self.name = name
self.link_idx = ...
# self.args = args
self.joint = None # joint name to struct
# self.join
self.children = ...
self.children = {} # joint name to child sruct
### dyn_model_act ###
# parent_rot_mtx, parent_trans_vec #
# parent_rot_mtx, parent_trans_vec # # link urdf #
# 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)
# expand visual pts #
def expand_visual_pts(self, expanded_visual_pts, link_name_to_visited, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
if self.visual is not None:
cur_expanded_visual_pts = self.visual.expand_visual_pts()
expanded_visual_pts.append(cur_expanded_visual_pts)
for cur_link in self.children:
cur_link_struct = link_name_to_link_struct[self.children[cur_link]]
cur_link_name = cur_link_struct.name
if cur_link_name in link_name_to_visited:
continue
expanded_visual_pts = cur_link_struct.expand_visual_pts(expanded_visual_pts, link_name_to_visited, link_name_to_link_struct)
return expanded_visual_pts
def get_transformed_visual_pts(self, visual_pts_list, link_name_to_visited, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
if self.joint is not None:
for cur_joint_name in self.joint:
cur_joint = self.joint[cur_joint_name]
cur_child_name = self.children[cur_joint_name]
cur_child = link_name_to_link_struct[cur_child_name] # parent and the child_visual, cur_child.visual #
# parent #
# print(f"joint: {cur_joint.name}, child: {cur_child_name}, parent: {self.name}, child_visual: {cur_child.visual is not None}")
# print(f"joint: {cur_joint.name}, child: {cur_child_name}, parent: {self.name}, child_visual: {cur_child.visual is not None}")
# joint_origin_xyz = cur_joint.origin_xyz ## transformed_visual_pts ####
if cur_child_name in link_name_to_visited:
continue
# cur_child_visual_pts = {'vertices': [], 'faces': [], 'link_idxes': [], 'transformed_joint_pos': [], 'joint_link_idxes': []}
cur_child_visual_pts_list = []
cur_child_visual_pts_list = cur_child.get_transformed_visual_pts(cur_child_visual_pts_list, link_name_to_visited, link_name_to_link_struct)
if len(cur_child_visual_pts_list) > 0:
cur_child_visual_pts = torch.cat(cur_child_visual_pts_list, dim=0)
# cur_child_verts, cur_child_faces = cur_child_visual_pts['vertices'], cur_child_visual_pts['faces']
# cur_child_link_idxes = cur_child_visual_pts['link_idxes']
# cur_transformed_joint_pos = cur_child_visual_pts['transformed_joint_pos']
# joint_link_idxes = cur_child_visual_pts['joint_link_idxes']
# if len(cur_child_verts) > 0:
# cur_child_verts, cur_child_faces = merge_meshes(cur_child_verts, cur_child_faces)
cur_child_visual_pts = cur_child_visual_pts + cur_joint.origin_xyz.unsqueeze(0)
cur_joint_rot, cur_joint_trans = cur_joint.compute_transformation_from_current_state()
cur_child_visual_pts = torch.matmul(cur_joint_rot, cur_child_visual_pts.transpose(1, 0).contiguous()).transpose(1, 0).contiguous() + cur_joint_trans.unsqueeze(0)
# if len(cur_transformed_joint_pos) > 0:
# cur_transformed_joint_pos = torch.cat(cur_transformed_joint_pos, dim=0)
# cur_transformed_joint_pos = cur_transformed_joint_pos + cur_joint.origin_xyz.unsqueeze(0)
# cur_transformed_joint_pos = torch.matmul(cur_joint_rot, cur_transformed_joint_pos.transpose(1, 0).contiguous()).transpose(1, 0).contiguous() + cur_joint_trans.unsqueeze(0)
# cur_joint_pos = cur_joint_trans.unsqueeze(0).clone()
# cur_transformed_joint_pos = torch.cat(
# [cur_transformed_joint_pos, cur_joint_pos], dim=0 ##### joint poses #####
# )
# else:
# cur_transformed_joint_pos = cur_joint_trans.unsqueeze(0).clone()
# if len(joint_link_idxes) > 0:
# joint_link_idxes = torch.cat(joint_link_idxes, dim=-1) ### joint_link idxes ###
# cur_joint_idx = cur_child.link_idx
# joint_link_idxes = torch.cat(
# [joint_link_idxes, torch.tensor([cur_joint_idx], dtype=torch.long).cuda()], dim=-1
# )
# else:
# joint_link_idxes = torch.tensor([cur_child.link_idx], dtype=torch.long).cuda().view(1,)
visual_pts_list.append(cur_child_visual_pts)
# cur_child_verts = cur_child_verts + # transformed joint pos #
# cur_child_link_idxes = torch.cat(cur_child_link_idxes, dim=-1)
# # joint_link_idxes = torch.cat(joint_link_idxes, dim=-1)
# init_visual_meshes['vertices'].append(cur_child_verts)
# init_visual_meshes['faces'].append(cur_child_faces)
# init_visual_meshes['link_idxes'].append(cur_child_link_idxes)
# init_visual_meshes['transformed_joint_pos'].append(cur_transformed_joint_pos)
# init_visual_meshes['joint_link_idxes'].append(joint_link_idxes)
if self.visual is not None:
# get_transformed_visual_pts #
visual_pts_list = self.visual.get_transformed_visual_pts(visual_pts_list)
# for cur_link in self.children:
# cur_link_name = cur_link.name
# if cur_link_name in link_name_to_visited: # link name to visited #
# continue
# visual_pts_list = cur_link.get_transformed_visual_pts(visual_pts_list, link_name_to_visited, link_name_to_link_struct)
return visual_pts_list
# use both the articulated motion and the frre form
def set_initial_state(self, states, action_joint_name_to_joint_idx, link_name_to_visited, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
if self.joint is not None:
for cur_joint_name in self.joint:
cur_joint = self.joint[cur_joint_name]
cur_joint_name = cur_joint.name
cur_child = self.children[cur_joint_name]
cur_child_struct = link_name_to_link_struct[cur_child]
cur_child_name = cur_child_struct.name
if cur_child_name in link_name_to_visited:
continue
if cur_joint.type in ['revolute']:
cur_joint_idx = action_joint_name_to_joint_idx[cur_joint_name] # action joint name to joint idx #
# cur_joint_idx = action_joint_name_to_joint_idx[cur_joint_name] #
# cur_joint = self.joint[cur_joint_name]
cur_state = states[cur_joint_idx] ### joint state ###
cur_joint.set_initial_state(cur_state)
cur_child_struct.set_initial_state(states, action_joint_name_to_joint_idx, link_name_to_visited, link_name_to_link_struct)
def get_init_visual_meshes(self, parent_rot, parent_trans, init_visual_meshes, link_name_to_link_struct, link_name_to_visited):
link_name_to_visited[self.name] = 1
# 'transformed_joint_pos': [], 'link_idxes': []
if self.joint is not None:
# for i_ch, (cur_joint, cur_child) in enumerate(zip(self.joint, self.children)):
# print(f"joint: {cur_joint.name}, child: {cur_child.name}, parent: {self.name}, child_visual: {cur_child.visual is not None}")
# joint_origin_xyz = cur_joint.origin_xyz
# init_visual_meshes = cur_child.get_init_visual_meshes(parent_rot, parent_trans + joint_origin_xyz, init_visual_meshes)
# print(f"name: {self.name}, keys: {self.joint.keys()}")
for cur_joint_name in self.joint: #
cur_joint = self.joint[cur_joint_name]
cur_child_name = self.children[cur_joint_name]
cur_child = link_name_to_link_struct[cur_child_name]
# print(f"joint: {cur_joint.name}, child: {cur_child_name}, parent: {self.name}, child_visual: {cur_child.visual is not None}")
# print(f"joint: {cur_joint.name}, child: {cur_child_name}, parent: {self.name}, child_visual: {cur_child.visual is not None}")
joint_origin_xyz = cur_joint.origin_xyz
if cur_child_name in link_name_to_visited:
continue
cur_child_visual_pts = {'vertices': [], 'faces': [], 'link_idxes': [], 'transformed_joint_pos': [], 'joint_link_idxes': []}
cur_child_visual_pts = cur_child.get_init_visual_meshes(parent_rot, parent_trans + joint_origin_xyz, cur_child_visual_pts, link_name_to_link_struct, link_name_to_visited)
cur_child_verts, cur_child_faces = cur_child_visual_pts['vertices'], cur_child_visual_pts['faces']
cur_child_link_idxes = cur_child_visual_pts['link_idxes']
cur_transformed_joint_pos = cur_child_visual_pts['transformed_joint_pos']
joint_link_idxes = cur_child_visual_pts['joint_link_idxes']
if len(cur_child_verts) > 0:
cur_child_verts, cur_child_faces = merge_meshes(cur_child_verts, cur_child_faces)
cur_child_verts = cur_child_verts + cur_joint.origin_xyz.unsqueeze(0)
cur_joint_rot, cur_joint_trans = cur_joint.compute_transformation_from_current_state()
cur_child_verts = torch.matmul(cur_joint_rot, cur_child_verts.transpose(1, 0).contiguous()).transpose(1, 0).contiguous() + cur_joint_trans.unsqueeze(0)
if len(cur_transformed_joint_pos) > 0:
cur_transformed_joint_pos = torch.cat(cur_transformed_joint_pos, dim=0)
cur_transformed_joint_pos = cur_transformed_joint_pos + cur_joint.origin_xyz.unsqueeze(0)
cur_transformed_joint_pos = torch.matmul(cur_joint_rot, cur_transformed_joint_pos.transpose(1, 0).contiguous()).transpose(1, 0).contiguous() + cur_joint_trans.unsqueeze(0)
cur_joint_pos = cur_joint_trans.unsqueeze(0).clone()
cur_transformed_joint_pos = torch.cat(
[cur_transformed_joint_pos, cur_joint_pos], dim=0 ##### joint poses #####
)
else:
cur_transformed_joint_pos = cur_joint_trans.unsqueeze(0).clone()
if len(joint_link_idxes) > 0:
joint_link_idxes = torch.cat(joint_link_idxes, dim=-1) ### joint_link idxes ###
cur_joint_idx = cur_child.link_idx
joint_link_idxes = torch.cat(
[joint_link_idxes, torch.tensor([cur_joint_idx], dtype=torch.long).cuda()], dim=-1
)
else:
joint_link_idxes = torch.tensor([cur_child.link_idx], dtype=torch.long).cuda().view(1,)
# cur_child_verts = cur_child_verts + # transformed joint pos #
cur_child_link_idxes = torch.cat(cur_child_link_idxes, dim=-1)
# joint_link_idxes = torch.cat(joint_link_idxes, dim=-1)
init_visual_meshes['vertices'].append(cur_child_verts)
init_visual_meshes['faces'].append(cur_child_faces)
init_visual_meshes['link_idxes'].append(cur_child_link_idxes)
init_visual_meshes['transformed_joint_pos'].append(cur_transformed_joint_pos)
init_visual_meshes['joint_link_idxes'].append(joint_link_idxes)
# joint_origin_xyz = self.joint.origin_xyz
else:
joint_origin_xyz = torch.tensor([0., 0., 0.], dtype=torch.float32).cuda()
# self.parent_rot_mtx = parent_rot
# self.parent_trans_vec = parent_trans + joint_origin_xyz
if self.visual is not None:
init_visual_meshes = self.visual.get_init_visual_meshes(parent_rot, parent_trans, init_visual_meshes)
cur_visual_mesh_pts_nn = self.visual.vertices.size(0)
cur_link_idxes = torch.zeros((cur_visual_mesh_pts_nn, ), dtype=torch.long).cuda()+ self.link_idx
init_visual_meshes['link_idxes'].append(cur_link_idxes)
# for cur_link in self.children: #
# init_visual_meshes = cur_link.get_init_visual_meshes(self.parent_rot_mtx, self.parent_trans_vec, init_visual_meshes)
return init_visual_meshes ## init visual meshes ##
# calculate inerti
def calculate_inertia(self, link_name_to_visited, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
self.cur_inertia = torch.zeros((3, 3), dtype=torch.float32).cuda()
if self.joint is not None:
for joint_nm in self.joint:
cur_joint = self.joint[joint_nm]
cur_child = self.children[joint_nm]
cur_child_struct = link_name_to_link_struct[cur_child]
cur_child_name = cur_child_struct.name
if cur_child_name in link_name_to_visited:
continue
joint_rot, joint_trans = cur_joint.compute_transformation_from_current_state(n_grad=True)
# cur_parent_rot = torch.matmul(parent_rot, joint_rot) #
# cur_parent_trans = torch.matmul(parent_rot, joint_trans.unsqueeze(-1)).squeeze(-1) + parent_trans #
child_inertia = cur_child_struct.calculate_inertia(link_name_to_visited, link_name_to_link_struct)
child_inertia = torch.matmul(
joint_rot.detach(), torch.matmul(child_inertia, joint_rot.detach().transpose(1, 0).contiguous())
).detach()
self.cur_inertia += child_inertia
# if self.visual is not None:
# self.cur_inertia += self.visual.inertia
self.cur_inertia += self.inertial.inertia.detach()
return self.cur_inertia
def set_delta_state_and_update(self, states, cur_timestep, link_name_to_visited, action_joint_name_to_joint_idx, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
if self.joint is not None:
for cur_joint_name in self.joint:
cur_joint = self.joint[cur_joint_name] # joint model
cur_child = self.children[cur_joint_name] # child model #
cur_child_struct = link_name_to_link_struct[cur_child]
cur_child_name = cur_child_struct.name
if cur_child_name in link_name_to_visited:
continue
## cur child inertia ##
# cur_child_inertia = cur_child_struct.cur_inertia
if cur_joint.type in ['revolute']:
cur_joint_idx = action_joint_name_to_joint_idx[cur_joint_name]
cur_state = states[cur_joint_idx]
### get the child struct ###
# set_actions_and_update_states(self, action, cur_timestep, time_cons, cur_inertia):
# set actions and update states #
cur_joint.set_delta_state_and_update(cur_state, cur_timestep)
cur_child_struct.set_delta_state_and_update(states, cur_timestep, link_name_to_visited, action_joint_name_to_joint_idx, link_name_to_link_struct)
# the joint #
# set_actions_and_update_states(actions, cur_timestep, time_cons, action_joint_name_to_joint_idx, link_name_to_visited, self.link_name_to_link_struct)
def set_actions_and_update_states(self, actions, cur_timestep, time_cons, action_joint_name_to_joint_idx, link_name_to_visited, link_name_to_link_struct):
link_name_to_visited[self.name] = 1
# the current joint of the
if self.joint is not None:
for cur_joint_name in self.joint:
cur_joint = self.joint[cur_joint_name] # joint model
cur_child = self.children[cur_joint_name] # child model #
cur_child_struct = link_name_to_link_struct[cur_child]
cur_child_name = cur_child_struct.name
if cur_child_name in link_name_to_visited:
continue
cur_child_inertia = cur_child_struct.cur_inertia
if cur_joint.type in ['revolute']:
cur_joint_idx = action_joint_name_to_joint_idx[cur_joint_name]
cur_action = actions[cur_joint_idx]
### get the child struct ###
# set_actions_and_update_states(self, action, cur_timestep, time_cons, cur_inertia):
# set actions and update states #
cur_joint.set_actions_and_update_states(cur_action, cur_timestep, time_cons, cur_child_inertia.detach())
cur_child_struct.set_actions_and_update_states(actions, cur_timestep, time_cons, action_joint_name_to_joint_idx, link_name_to_visited, link_name_to_link_struct)
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
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)
class Joint_Limit:
def __init__(self, effort, lower, upper, velocity) -> None:
self.effort = effort
self.lower = lower
self.velocity = velocity
self.upper = upper
pass
# Joint_urdf(name, joint_type, parent_link, child_link, origin_xyz, axis_xyz, limit: Joint_Limit)
class Joint_urdf: #
def __init__(self, name, joint_type, parent_link, child_link, origin_xyz, axis_xyz, limit: Joint_Limit) -> None:
self.name = name
self.type = joint_type
self.parent_link = parent_link
self.child_link = child_link
self.origin_xyz = origin_xyz
self.axis_xyz = axis_xyz
self.limit = limit
# joint angle; joint state #
self.timestep_to_vels = {}
self.timestep_to_states = {}
self.init_pos = self.origin_xyz.clone()
#### only for the current state #### # joint urdf #
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)
def set_initial_state(self, state):
# joint angle as the state value #
self.timestep_to_vels[0] = torch.zeros((3,), dtype=torch.float32).cuda().detach() ## velocity ##
delta_rot_vec = self.axis_xyz * state
# self.timestep_to_states[0] = state.detach()
cur_state = torch.tensor([1., 0., 0., 0.], dtype=torch.float32).cuda()
init_state = cur_state + update_quaternion(delta_rot_vec, cur_state)
self.timestep_to_states[0] = init_state.detach()
self.state = init_state
def set_delta_state_and_update(self, state, cur_timestep):
self.timestep_to_vels[cur_timestep] = torch.zeros((3,), dtype=torch.float32).cuda().detach()
delta_rot_vec = self.axis_xyz * state
if cur_timestep == 0:
prev_state = torch.tensor([1., 0., 0., 0.], dtype=torch.float32).cuda()
else:
prev_state = self.timestep_to_states[cur_timestep - 1].detach()
cur_state = prev_state + update_quaternion(delta_rot_vec, prev_state)
self.timestep_to_states[cur_timestep] = cur_state.detach()
self.state = cur_state
def compute_transformation_from_current_state(self, n_grad=False):
# together with the parent rot mtx and the parent trans vec #
# cur_joint_state = self.state
if self.type == "revolute":
# rot_mtx = rotation_matrix_from_axis_angle(self.axis, cur_joint_state)
# trans_vec = self.pos - np.matmul(rot_mtx, self.pos.reshape(3, 1)).reshape(3)
if n_grad:
rot_mtx = quaternion_to_matrix(self.state.detach())
else:
rot_mtx = quaternion_to_matrix(self.state)
# trans_vec = self.pos - torch.matmul(rot_mtx, self.pos.view(3, 1)).view(3).contiguous()
trans_vec = self.origin_xyz - torch.matmul(rot_mtx, self.origin_xyz.view(3, 1)).view(3).contiguous()
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec
elif self.type == "fixed":
rot_mtx = torch.eye(3, dtype=torch.float32).cuda()
trans_vec = torch.zeros((3,), dtype=torch.float32).cuda()
# trans_vec = self.origin_xyz
self.rot_mtx = rot_mtx
self.trans_vec = trans_vec #
else:
pass
return self.rot_mtx, self.trans_vec
# set actions # set actions and udpate states #
def set_actions_and_update_states(self, action, cur_timestep, time_cons, cur_inertia):
# timestep_to_vels, timestep_to_states, state #
if self.type in ['revolute']:
self.action = action
#
# visual_pts and visual_pts_mass #
# cur_joint_pos = self.joint.pos #
# TODO: check whether the following is correct #
torque = self.action * self.axis_xyz
# # 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
# inertia_inv = self.cur_inertia_inv
# print(f"updating actions and states for the joint {self.name} with type {self.type}")
inertia_inv = torch.linalg.inv(cur_inertia).detach()
delta_omega = torch.matmul(inertia_inv, torque.unsqueeze(-1)).squeeze(-1)
# delta_omega = torque / 400 #
# timestep_to_vels, timestep_to_states, state #
# 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_angular_vel = delta_angular_vel.squeeze(0)
if cur_timestep > 0: ## cur_timestep - 1 ##
prev_angular_vel = self.timestep_to_vels[cur_timestep - 1].detach()
cur_angular_vel = prev_angular_vel + delta_angular_vel * DAMPING
else:
cur_angular_vel = delta_angular_vel
self.timestep_to_vels[cur_timestep] = cur_angular_vel.detach()
cur_delta_quat = cur_angular_vel * time_cons # * self.args.dt
cur_delta_quat = cur_delta_quat.squeeze(0)
cur_state = self.timestep_to_states[cur_timestep].detach() # quaternion #
# print(f"cur_delta_quat: {cur_delta_quat.size()}, cur_state: {cur_state.size()}")
nex_state = cur_state + update_quaternion(cur_delta_quat, cur_state)
self.timestep_to_states[cur_timestep + 1] = nex_state.detach()
self.state = nex_state # set the joint state #
# get_transformed_visual_pts() --- transformed_visual_pts ##
# use the transformed visual # the articulated motion field #
# then we should add the free motion field here # # add the free motion field # # hwo to use that? #
# another rules for optimizing articulation motion field #
# -> the articulated model predicted transformations #
# -> the free motion field -> the motion field predicted by the network for each timestep -> an implicit motion field #
class Robot_urdf:
def __init__(self, links, link_name_to_link_idxes, link_name_to_link_struct, joint_name_to_joint_idx, actions_joint_name_to_joint_idx) -> None:
self.links = links
self.link_name_to_link_idxes = link_name_to_link_idxes
self.link_name_to_link_struct = link_name_to_link_struct
# joint_name_to_joint_idx, actions_joint_name_to_joint_idx
self.joint_name_to_joint_idx = joint_name_to_joint_idx
self.actions_joint_name_to_joint_idx = actions_joint_name_to_joint_idx
#
# particles
# sample particles
# how to sample particles?
# how to expand the particles? # -> you can use weights in the model dict #
# from grids and jample from grids #
# link idx to the
# robot #
# init vertices, init faces #
# expande the aprticles #
# expanede particles #
# use particles to conduct the simulation #
self.init_vertices, self.init_faces = self.get_init_visual_pts()
init_visual_pts_sv_fn = "robot_expanded_visual_pts.npy"
np.save(init_visual_pts_sv_fn, self.init_vertices.detach().cpu().numpy())
joint_name_to_joint_idx_sv_fn = "mano_joint_name_to_joint_idx.npy"
np.save(joint_name_to_joint_idx_sv_fn, self.joint_name_to_joint_idx)
actions_joint_name_to_joint_idx_sv_fn = "mano_actions_joint_name_to_joint_idx.npy"
np.save(actions_joint_name_to_joint_idx_sv_fn, self.actions_joint_name_to_joint_idx)
tot_joints = len(self.joint_name_to_joint_idx)
tot_actions_joints = len(self.actions_joint_name_to_joint_idx)
print(f"tot_joints: {tot_joints}, tot_actions_joints: {tot_actions_joints}")
pass
def expand_visual_pts(self, ):
link_name_to_visited = {}
# transform the visual pts #
# action_joint_name_to_joint_idx = self.actions_joint_name_to_joint_idx
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
expanded_visual_pts = []
# expanded the visual pts # # transformed viusal pts # or the translations of the visual pts #
expanded_visual_pts = palm_link.expand_visual_pts(expanded_visual_pts, link_name_to_visited, self.link_name_to_link_struct)
expanded_visual_pts = torch.cat(expanded_visual_pts, dim=0)
# pass
return expanded_visual_pts
# get_transformed_visual_pts() # get_transformed_visual_pts of the visual pts ### get_transformed_visual_pts ## get_transformed_visual_pts ### #
def get_transformed_visual_pts(self, ):
init_visual_pts = []
link_name_to_visited = {}
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
### init_visual_pts # from the pal mink to get the total transformed visual pts ##
init_visual_pts = palm_link.get_transformed_visual_pts(init_visual_pts, link_name_to_visited, self.link_name_to_link_struct)
init_visual_pts = torch.cat(init_visual_pts, dim=0) ## get the inita visual pts from the palm link ###
return init_visual_pts
### samping issue? --- TODO` `
def get_init_visual_pts(self, ):
init_visual_meshes = {
'vertices': [], 'faces': [], 'link_idxes': [], 'transformed_joint_pos': [], 'link_idxes': [], 'transformed_joint_pos': [], 'joint_link_idxes': []
}
init_parent_rot = torch.eye(3, dtype=torch.float32).cuda()
init_parent_trans = torch.zeros((3,), dtype=torch.float32).cuda()
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
link_name_to_visited = {}
init_visual_meshes = palm_link.get_init_visual_meshes(init_parent_rot, init_parent_trans, init_visual_meshes, self.link_name_to_link_struct, link_name_to_visited)
self.link_idxes = torch.cat(init_visual_meshes['link_idxes'], dim=-1)
self.transformed_joint_pos = torch.cat(init_visual_meshes['transformed_joint_pos'], dim=0)
self.joint_link_idxes = torch.cat(init_visual_meshes['joint_link_idxes'], dim=-1) ###
# for cur_link in self.links:
# init_visual_meshes = cur_link.get_init_visual_meshes(init_parent_rot, init_parent_trans, init_visual_meshes, self.link_name_to_link_struct, link_name_to_visited)
init_vertices, init_faces = merge_meshes(init_visual_meshes['vertices'], init_visual_meshes['faces'])
return init_vertices, init_faces
def set_delta_state_and_update(self, states, cur_timestep):
link_name_to_visited = {}
action_joint_name_to_joint_idx = self.actions_joint_name_to_joint_idx
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
link_name_to_visited = {}
palm_link.set_delta_state_and_update(states, cur_timestep, link_name_to_visited, action_joint_name_to_joint_idx, self.link_name_to_link_struct)
# cur_joint.set_actions_and_update_states(cur_action, cur_timestep, time_cons, cur_child_inertia)
def set_actions_and_update_states(self, actions, cur_timestep, time_cons,):
# actions
# self.actions_joint_name_to_joint_idx as the action joint name to joint idx
link_name_to_visited = {}
action_joint_name_to_joint_idx = self.actions_joint_name_to_joint_idx
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
link_name_to_visited = {}
palm_link.set_actions_and_update_states(actions, cur_timestep, time_cons, action_joint_name_to_joint_idx, link_name_to_visited, self.link_name_to_link_struct)
# for cur_joint in
# for cur_link in self.links:
# if cur_link.joint is not None:
# for cur_joint_nm in cur_link.joint:
# if cur_link.joint[cur_joint_nm].type in ['revolute']:
# cur_link_joint_name = cur_link.joint[cur_joint_nm].name
# cur_link_joint_idx = self.actions_joint_name_to_joint_idx[cur_link_joint_name]
# for cur_link in self.links:
# cur_link.set_actions_and_update_states(actions, cur_timestep, time_cons, action_joint_name_to_joint_idx, link_name_to_visited, self.link_name_to_link_struct)
### TODO: add the contact torque when calculating the nextstep states ###
### TODO: not an accurate implementation since differen joints should be jconsidered for one single link ###
### TODO: the articulated force modle is not so easy as this one .... ###
def set_contact_forces(self, hard_selected_forces, hard_selected_manipulating_points, hard_selected_sampled_input_pts_idxes):
# transformed_joint_pos, joint_link_idxes, link_idxes #
selected_pts_link_idxes = self.link_idxes[hard_selected_sampled_input_pts_idxes]
# use the selected link idxes #
# selected pts idxes #
# self.joint_link_idxes, transformed_joint_pos #
self.link_idx_to_transformed_joint_pos = {}
for i_link in range(self.transformed_joint_pos.size(0)):
cur_link_idx = self.link_idxes[i_link].item()
cur_link_pos = self.transformed_joint_pos[i_link]
# if cur_link_idx not in self.link_idx_to_transformed_joint_pos:
self.link_idx_to_transformed_joint_pos[cur_link_idx] = cur_link_pos
# self.link_idx_to_transformed_joint_pos[cur_link_idx].append(cur_link_pos)
# from the
self.link_idx_to_contact_forces = {}
for i_c_pts in range(hard_selected_forces.size(0)):
cur_contact_force = hard_selected_forces[i_c_pts] ##
cur_link_idx = selected_pts_link_idxes[i_c_pts].item()
cur_link_pos = self.link_idx_to_transformed_joint_pos[cur_link_idx]
cur_link_action_pos = hard_selected_manipulating_points[i_c_pts]
# (action_pos - link_pos) x (-contact_force) #
cur_contact_torque = torch.cross(
cur_link_action_pos - cur_link_pos, -cur_contact_force
)
if cur_link_idx not in self.link_idx_to_contact_forces:
self.link_idx_to_contact_forces[cur_link_idx] = [cur_contact_torque]
else:
self.link_idx_to_contact_forces[cur_link_idx].append(cur_contact_torque)
for link_idx in self.link_idx_to_contact_forces:
self.link_idx_to_contact_forces[link_idx] = torch.stack(self.link_idx_to_contact_forces[link_idx], dim=0)
self.link_idx_to_contact_forces[link_idx] = torch.sum(self.link_idx_to_contact_forces[link_idx] , dim=0)
for link_idx, link_struct in enumerate(self.links):
if link_idx in self.link_idx_to_contact_forces:
cur_link_contact_force = self.link_idx_to_contact_forces[link_idx]
link_struct.contact_torque = cur_link_contact_force
else:
link_struct.contact_torque = None
# def se ### from the optimizable initial states ###
def set_initial_state(self, states):
action_joint_name_to_joint_idx = self.actions_joint_name_to_joint_idx
link_name_to_visited = {}
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
link_name_to_visited = {}
palm_link.set_initial_state(states, action_joint_name_to_joint_idx, link_name_to_visited, self.link_name_to_link_struct)
# for cur_link in self.links:
# cur_link.set_initial_state(states, action_joint_name_to_joint_idx, link_name_to_visited, self.link_name_to_link_struct)
### after each timestep -> re-calculate the inertial matrix using the current simulated states and the set the new actiosn and forward the simulation #
def calculate_inertia(self):
link_name_to_visited = {}
palm_idx = self.link_name_to_link_idxes["palm"]
palm_link = self.links[palm_idx]
link_name_to_visited = {}
palm_link.calculate_inertia(link_name_to_visited, self.link_name_to_link_struct)
# for cur_link in self.links:
# cur_link.calculate_inertia(link_name_to_visited, self.link_name_to_link_struct)
def parse_nparray_from_string(strr, args=None):
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
### parse link data ###
def parse_link_data_urdf(link):
link_name = link.attrib["name"]
# print(f"parsing link: {link_name}") ## joints body meshes #
inertial = link.find("./inertial")
origin = inertial.find("./origin")
inertial_pos = origin.attrib["xyz"]
inertial_pos = parse_nparray_from_string(inertial_pos)
inertial_rpy = origin.attrib["rpy"]
inertial_rpy = parse_nparray_from_string(inertial_rpy)
inertial_mass = inertial.find("./mass")
inertial_mass = inertial_mass.attrib["value"]
inertial_inertia = inertial.find("./inertia")
inertial_ixx = inertial_inertia.attrib["ixx"]
inertial_ixx = float(inertial_ixx)
inertial_ixy = inertial_inertia.attrib["ixy"]
inertial_ixy = float(inertial_ixy)
inertial_ixz = inertial_inertia.attrib["ixz"]
inertial_ixz = float(inertial_ixz)
inertial_iyy = inertial_inertia.attrib["iyy"]
inertial_iyy = float(inertial_iyy)
inertial_iyz = inertial_inertia.attrib["iyz"]
inertial_iyz = float(inertial_iyz)
inertial_izz = inertial_inertia.attrib["izz"]
inertial_izz = float(inertial_izz)
inertial_inertia_mtx = torch.zeros((3, 3), dtype=torch.float32).cuda()
inertial_inertia_mtx[0, 0] = inertial_ixx
inertial_inertia_mtx[0, 1] = inertial_ixy
inertial_inertia_mtx[0, 2] = inertial_ixz
inertial_inertia_mtx[1, 0] = inertial_ixy
inertial_inertia_mtx[1, 1] = inertial_iyy
inertial_inertia_mtx[1, 2] = inertial_iyz
inertial_inertia_mtx[2, 0] = inertial_ixz
inertial_inertia_mtx[2, 1] = inertial_iyz
inertial_inertia_mtx[2, 2] = inertial_izz
# [xx, xy, xz] #
# [0, yy, yz] #
# [0, 0, zz] #
# a strange inertia value ... #
# TODO: how to compute the inertia matrix? #
visual = link.find("./visual")
if visual is not None:
origin = visual.find("./origin")
visual_pos = origin.attrib["xyz"]
visual_pos = parse_nparray_from_string(visual_pos)
visual_rpy = origin.attrib["rpy"]
visual_rpy = parse_nparray_from_string(visual_rpy)
geometry = visual.find("./geometry")
geometry_mesh = geometry.find("./mesh")
mesh_fn = geometry_mesh.attrib["filename"]
mesh_scale = geometry_mesh.attrib["scale"]
mesh_scale = parse_nparray_from_string(mesh_scale)
mesh_fn = str(mesh_fn)
link_struct = Link_urdf(name=link_name, inertial=Inertial(origin_rpy=inertial_rpy, origin_xyz=inertial_pos, mass=inertial_mass, inertia=inertial_inertia_mtx), visual=Visual(visual_rpy=visual_rpy, visual_xyz=visual_pos, geometry_mesh_fn=mesh_fn, geometry_mesh_scale=mesh_scale) if visual is not None else None)
return link_struct
def parse_joint_data_urdf(joint):
joint_name = joint.attrib["name"]
joint_type = joint.attrib["type"]
parent = joint.find("./parent")
child = joint.find("./child")
parent_name = parent.attrib["link"]
child_name = child.attrib["link"]
joint_origin = joint.find("./origin")
# if joint_origin.
try:
origin_xyz = joint_origin.attrib["xyz"]
origin_xyz = parse_nparray_from_string(origin_xyz)
except:
origin_xyz = torch.tensor([0., 0., 0.], dtype=torch.float32).cuda()
joint_axis = joint.find("./axis")
if joint_axis is not None:
joint_axis = joint_axis.attrib["xyz"]
joint_axis = parse_nparray_from_string(joint_axis)
else:
joint_axis = torch.tensor([1, 0., 0.], dtype=torch.float32).cuda()
joint_limit = joint.find("./limit")
if joint_limit is not None:
joint_lower = joint_limit.attrib["lower"]
joint_lower = float(joint_lower)
joint_upper = joint_limit.attrib["upper"]
joint_upper = float(joint_upper)
joint_effort = joint_limit.attrib["effort"]
joint_effort = float(joint_effort)
joint_velocity = joint_limit.attrib["velocity"]
joint_velocity = float(joint_velocity)
else:
joint_lower = -0.5000
joint_upper = 1.57
joint_effort = 1000
joint_velocity = 0.5
# cosntruct the joint data #
joint_limit = Joint_Limit(effort=joint_effort, lower=joint_lower, upper=joint_upper, velocity=joint_velocity)
cur_joint_struct = Joint_urdf(joint_name, joint_type, parent_name, child_name, origin_xyz, joint_axis, joint_limit)
return cur_joint_struct
def parse_data_from_urdf(xml_fn):
tree = ElementTree()
tree.parse(xml_fn)
print(f"{xml_fn}")
### get total robots ###
# robots = tree.findall("link")
cur_robot = tree
# i_robot = 0 #
# tot_robots = [] #
# for cur_robot in robots: #
# print(f"Getting robot: {i_robot}") #
# i_robot += 1 #
# print(f"len(robots): {len(robots)}") #
# cur_robot = robots[0] #
cur_links = cur_robot.findall("./link")
# i_link = 0
link_name_to_link_idxes = {}
cur_robot_links = []
link_name_to_link_struct = {}
for i_link_idx, cur_link in enumerate(cur_links):
cur_link_struct = parse_link_data_urdf(cur_link)
print(f"Adding link {cur_link_struct.name}")
cur_link_struct.link_idx = i_link_idx
cur_robot_links.append(cur_link_struct)
link_name_to_link_idxes[cur_link_struct.name] = i_link_idx
link_name_to_link_struct[cur_link_struct.name] = cur_link_struct
# for cur_link in cur_links:
# cur_robot_links.append(parse_link_data_urdf(cur_link, args=args))
print(f"link_name_to_link_struct: {len(link_name_to_link_struct)}, ")
tot_robot_joints = []
joint_name_to_joint_idx = {}
actions_joint_name_to_joint_idx = {}
cur_joints = cur_robot.findall("./joint")
for i_joint, cur_joint in enumerate(cur_joints):
cur_joint_struct = parse_joint_data_urdf(cur_joint)
cur_joint_parent_link = cur_joint_struct.parent_link
cur_joint_child_link = cur_joint_struct.child_link
cur_joint_idx = len(tot_robot_joints)
cur_joint_name = cur_joint_struct.name
joint_name_to_joint_idx[cur_joint_name] = cur_joint_idx
cur_joint_type = cur_joint_struct.type
if cur_joint_type in ['revolute']:
actions_joint_name_to_joint_idx[cur_joint_name] = cur_joint_idx
#### add the current joint to tot joints ###
tot_robot_joints.append(cur_joint_struct)
parent_link_idx = link_name_to_link_idxes[cur_joint_parent_link]
cur_parent_link_struct = cur_robot_links[parent_link_idx]
child_link_idx = link_name_to_link_idxes[cur_joint_child_link]
cur_child_link_struct = cur_robot_links[child_link_idx]
# parent link struct #
if link_name_to_link_struct[cur_joint_parent_link].joint is not None:
link_name_to_link_struct[cur_joint_parent_link].joint[cur_joint_struct.name] = cur_joint_struct
link_name_to_link_struct[cur_joint_parent_link].children[cur_joint_struct.name] = cur_child_link_struct.name
# cur_child_link_struct
# cur_parent_link_struct.joint.append(cur_joint_struct)
# cur_parent_link_struct.children.append(cur_child_link_struct)
else:
link_name_to_link_struct[cur_joint_parent_link].joint = {
cur_joint_struct.name: cur_joint_struct
}
link_name_to_link_struct[cur_joint_parent_link].children = {
cur_joint_struct.name: cur_child_link_struct.name
# cur_child_link_struct
}
# cur_parent_link_struct.joint = [cur_joint_struct]
# cur_parent_link_struct.children.append(cur_child_link_struct)
# pass
cur_robot_obj = Robot_urdf(cur_robot_links, link_name_to_link_idxes, link_name_to_link_struct, joint_name_to_joint_idx, actions_joint_name_to_joint_idx)
# tot_robots.append(cur_robot_obj)
# for the joint robots #
# for every joint
# 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 cur_robot_obj
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) -> None:
self.xml_fn = xml_fn
# self.args = args
##
active_robot = parse_data_from_urdf(xml_fn)
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 #
self.optimizable_actions = nn.Embedding(
num_embeddings=100, embedding_dim=60,
).cuda()
torch.nn.init.zeros_(self.optimizable_actions.weight) #
self.learning_rate = 5e-4
self.active_robot = active_robot
# # get init states # #
self.set_init_states()
init_visual_pts = self.get_init_state_visual_pts()
self.init_visual_pts = init_visual_pts
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)
# init_joint_states = torch.zeros((60, ), dtype=torch.float32).cuda()
self.active_robot.set_initial_state(init_states)
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 #
# init_states['glb_rot'] = glb_rot; #
# init_states['glb_trans'] = glb_trans;
# self.active_robot.set_init_states(init_states)
init_joint_states = torch.zeros((60, ), dtype=torch.float32).cuda()
self.active_robot.set_initial_state(init_joint_states)
def get_init_state_visual_pts(self, ):
# visual_pts_list = [] # compute the transformation via current state #
# visual_pts_list, visual_pts_mass_list = self.active_robot.compute_transformation_via_current_state( visual_pts_list)
cur_verts, cur_faces = self.active_robot.get_init_visual_pts()
self.faces = cur_faces
# init_visual_pts = visual_pts_list
return cur_verts
def set_actions_and_update_states(self, actions, cur_timestep):
#
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) ###
pass
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 = 100
# 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 // 2 + 1: torch.from_numpy(self.ts_to_reference_pts[ts]).float().cuda() for ts in self.ts_to_reference_pts
}
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 #
with torch.no_grad():
self.active_robot.calculate_inertia()
self.active_robot.set_actions_and_update_states(actions_link_actions, cur_ts, 0.2)
cur_visual_pts, cur_faces = self.active_robot.get_init_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(retain_graph=True)
# diff.backward(retain_graph=False)
self.optimizer.step()
tot_losses.append(diff.item())
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 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
#### Big TODO: the external contact forces from the manipulated object to the robot ####
if __name__=='__main__':
urdf_fn = "/home/xueyi/diffsim/NeuS/rsc/mano/mano_mean_nocoll_simplified.urdf"
robot_agent = RobotAgent(urdf_fn)
ref_dict_npy = "reference_verts.npy"
robot_agent.initialize_optimization(ref_dict_npy)
ts_to_robot_points, ts_to_ref_points = robot_agent.forward_stepping_optimization()
np.save(f"ts_to_robot_points.npy", ts_to_robot_points)
np.save(f"ts_to_ref_points.npy", ts_to_ref_points)
exit(0)
urdf_fn = "/home/xueyi/diffsim/NeuS/rsc/mano/mano_mean_nocoll_simplified.urdf"
cur_robot = parse_data_from_urdf(urdf_fn)
# self.init_vertices, self.init_faces
init_vertices, init_faces = cur_robot.init_vertices, cur_robot.init_faces
init_vertices = init_vertices.detach().cpu().numpy()
init_faces = init_faces.detach().cpu().numpy()
## initial states ehre ##3
# mesh_obj = trimesh.Trimesh(vertices=init_vertices, faces=init_faces)
# mesh_obj.export(f"hand_urdf.ply")
##### Test the set initial state function #####
init_joint_states = torch.zeros((60, ), dtype=torch.float32).cuda()
cur_robot.set_initial_state(init_joint_states)
##### Test the set initial state function #####
cur_zeros_actions = torch.zeros((60, ), dtype=torch.float32).cuda()
cur_ones_actions = torch.ones((60, ), dtype=torch.float32).cuda() # * 100
ts_to_mesh_verts = {}
for i_ts in range(50):
cur_robot.calculate_inertia()
cur_robot.set_actions_and_update_states(cur_ones_actions, i_ts, 0.2) ###
cur_verts, cur_faces = cur_robot.get_init_visual_pts()
cur_mesh = trimesh.Trimesh(vertices=cur_verts.detach().cpu().numpy(), faces=cur_faces.detach().cpu().numpy())
ts_to_mesh_verts[i_ts + i_ts] = cur_verts.detach().cpu().numpy()
# cur_mesh.export(f"stated_mano_mesh.ply")
# cur_mesh.export(f"zero_actioned_mano_mesh.ply")
cur_mesh.export(f"ones_actioned_mano_mesh_ts_{i_ts}.ply")
np.save(f"reference_verts.npy", ts_to_mesh_verts)
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()
exit(0)
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