RoboTwin_image/envs/shoes_place.py
2025-07-02 03:13:07 +00:00

81 lines
3.5 KiB
Python

from .base_task import Base_task
from .utils import *
import math
import sapien
class shoes_place(Base_task):
def setup_demo(self,is_test = False, **kwags):
super()._init(**kwags)
self.create_table_and_wall()
self.load_robot()
self.setup_planner()
self.load_camera(kwags.get('camera_w', 640),kwags.get('camera_h', 480))
self.pre_move()
if is_test:
self.id_list = [2*i+1 for i in range(5)]
else:
self.id_list = [2*i for i in range(5)]
self.load_actors(f"./task_config/scene_info/{self.task_name[4:]}.json")
self.step_lim = 600
def pre_move(self):
render_freq = self.render_freq
self.render_freq=0
self.together_open_gripper(save_freq=None)
self.render_freq = render_freq
def create_block_data(self, half_size):
contact_discription_list = []
test_matrix = np.array([[0,0,1,0],[1,0,0,0],[0,1,0,0],[0,0,0,1]])
test_matrix[:3,:3] = t3d.euler.euler2mat(0,0,np.pi) @ test_matrix[:3,:3]
# print(test_matrix.tolist())
contact_points_list = []
data = {
'center': [0,0,0],
'extents': half_size,
'scale': [1,1,1], # 缩放
'target_pose': [[[1,0,0,0],[0,1,0,-0.06],[0,0,1,half_size[2]],[0,0,0,1]], [[1,0,0,0],[0,1,0,0.06],[0,0,1,half_size[2]],[0,0,0,1]]], # 目标点矩阵
'contact_points_pose' : contact_points_list, # 抓取点矩阵(多个)
'transform_matrix': np.eye(4).tolist(), # 模型到标轴的旋转矩阵
"functional_matrix": [[0., 1., 0., 0.], [0., 0., -1., 0.], [1., 0., 0., 0.], [0., 0., 0., 1.]], # 功能点矩阵
'contact_points_discription': contact_discription_list, # 抓取点描述
'contact_points_group': [],
'contact_points_mask': [],
'target_point_discription': ["The center point on the top of the box." ]
}
return data
def get_target_grap_pose(self,shoe_rpy):
if math.fmod(math.fmod(shoe_rpy[2] + shoe_rpy[0], 2 * math.pi) + 2 * math.pi, 2*math.pi) < math.pi:
grasp_matrix = np.array([[-1,0,0,0],[0,1,0,0],[0,0,-1,0],[0,0,0,1]])
target_quat = [-0.707,0,-0.707,0]
else:
grasp_matrix = np.eye(4)
target_quat = [0,0.707,0,-0.707]
return grasp_matrix, target_quat
def play_once(self):
pass
def check_success(self):
left_shoe = self.actor_name_dic['left_shoe']
right_shoe = self.actor_name_dic['right_shoe']
left_shoe_pose_p = np.array(left_shoe.get_pose().p)
left_shoe_pose_q = np.array(left_shoe.get_pose().q)
right_shoe_pose_p = np.array(right_shoe.get_pose().p)
right_shoe_pose_q = np.array(right_shoe.get_pose().q)
if left_shoe_pose_q[0] < 0:
left_shoe_pose_q *= -1
if right_shoe_pose_q[0] < 0:
right_shoe_pose_q *= -1
target_pose_p = np.array([0,-0.13])
target_pose_q = np.array([0.5,0.5,-0.5,-0.5])
eps = np.array([0.02,0.02,0.05,0.05,0.05,0.05])
return np.all(abs(left_shoe_pose_p[:2] - (target_pose_p - [0,0.06])) < eps[:2]) and np.all(abs(left_shoe_pose_q - target_pose_q) < eps[-4:]) and \
np.all(abs(right_shoe_pose_p[:2] - (target_pose_p + [0,0.06])) < eps[:2]) and np.all(abs(right_shoe_pose_q - target_pose_q) < eps[-4:]) and self.is_left_gripper_open() and self.is_right_gripper_open()