from copy import deepcopy import numpy as np from core.skills.base_skill import BaseSkill, register_skill from omegaconf import DictConfig from omni.isaac.core.controllers import BaseController from omni.isaac.core.robots.robot import Robot from omni.isaac.core.tasks import BaseTask from omni.isaac.core.utils.prims import get_prim_at_path from omni.isaac.core.utils.transformations import ( get_relative_transform, pose_from_tf_matrix, tf_matrix_from_pose, ) from scipy.spatial.transform import Rotation as R # pylint: disable=unused-argument @register_skill class Flip(BaseSkill): def __init__(self, robot: Robot, controller: BaseController, task: BaseTask, cfg: DictConfig, *args, **kwargs): super().__init__() self.robot = robot self.controller = controller self.task = task self.name = cfg["name"] self.pick_obj = task.objects[cfg["objects"][0]] self.skill_cfg = cfg self.gripper_axis = cfg.get("gripper_axis", False) self.manip_list = [] if kwargs: self.draw = kwargs["draw"] if "left" in self.controller.robot_file: self.robot_ee_path = self.robot.fl_ee_path self.robot_base_path = self.robot.fl_base_path elif "right" in self.controller.robot_file: self.robot_ee_path = self.robot.fr_ee_path self.robot_base_path = self.robot.fr_base_path def simple_generate_manip_cmds(self): manip_list = [] place_traj, post_place_level = self.sample_place_traj() if len(place_traj) > 1: # Having waypoints for waypoint in place_traj[:-1]: p_base_ee, q_base_ee = waypoint[:3], waypoint[3:] cmd = (p_base_ee, q_base_ee, "close_gripper", {}) manip_list.append(cmd) # The last waypoint p_base_ee_place, q_base_ee_place = place_traj[-1][:3], place_traj[-1][3:] cmd = (p_base_ee_place, q_base_ee_place, "close_gripper", {}) manip_list.append(cmd) cmd = (p_base_ee_place, q_base_ee_place, "open_gripper", {}) manip_list.extend( [cmd] * self.skill_cfg.get("open_wait_steps", 20) ) # here we use 20 steps to make sure the gripper is fully open # Adding a pose place pose to avoid collision when combining place skill and close skill T_base_ee_place = tf_matrix_from_pose(p_base_ee_place, q_base_ee_place) # Post place T_base_ee_postplace = deepcopy(T_base_ee_place) # Retreat for a bit along gripper axis T_base_ee_postplace[0:3, 3] = T_base_ee_postplace[0:3, 3] - T_base_ee_postplace[0:3, 0] * post_place_level cmd = (*pose_from_tf_matrix(T_base_ee_postplace), "open_gripper", {}) manip_list.append(cmd) self.manip_list = manip_list def sample_place_traj(self): place_traj = [] T_base_ee = get_relative_transform(get_prim_at_path(self.robot_ee_path), get_prim_at_path(self.robot_base_path)) T_world_base = get_relative_transform(get_prim_at_path(self.robot_base_path), get_prim_at_path("/World")) T_world_ee = T_world_base @ T_base_ee # 1. Obtaining ee_ori gripper_axis = np.array(self.gripper_axis) gripper_axis = gripper_axis / np.linalg.norm(gripper_axis) # Normalize the vector camera_axis = np.array([0, 1, 0]) q_world_ee = self.get_ee_ori(gripper_axis, T_world_ee, camera_axis) # 2. Obtaining ee_trans p_world_ee_init = self.controller.T_world_ee_init[0:3, 3] # getting initial ee position p_world_ee = p_world_ee_init.copy() p_world_ee[0] += np.random.uniform(0.19, 0.21) # 0.2 p_world_ee[1] += np.random.uniform(0.23, 0.27) # 0.25 p_world_ee[2] += 0 if self.draw: self.draw.draw_points([p_world_ee.tolist()], [(1, 0, 0, 1)], [7]) # red # 3. Adding waypoint # Pre place p_world_ee_mid = p_world_ee_init.copy() p_world_ee_mid[1] += np.random.uniform(0.23, 0.27) # 0.25 p_world_ee_mid[2] += np.random.uniform(0.14, 0.16) # 0.15 gripper_axis_mid = np.array([0, 1, -1]) camera_axis_mid = np.array([0, 1, 1]) gripper_axis_mid = gripper_axis_mid / np.linalg.norm(gripper_axis_mid) camera_axis_mid = camera_axis_mid / np.linalg.norm(camera_axis_mid) q_world_ee_mid = self.get_ee_ori(gripper_axis_mid, T_world_ee, camera_axis_mid) if self.draw: self.draw.draw_points([p_world_ee_mid.tolist()], [(1, 0, 0, 1)], [7]) # red place_traj.append(self.adding_waypoint(p_world_ee_mid, q_world_ee_mid, T_world_base)) # Place place_traj.append(self.adding_waypoint(p_world_ee, q_world_ee, T_world_base)) post_place_level = 0.1 return place_traj, post_place_level def get_ee_ori(self, gripper_axis, T_world_ee, camera_axis=None): gripper_x = gripper_axis if camera_axis is not None: gripper_z = camera_axis else: current_z = T_world_ee[0:3, 1] gripper_z = current_z - np.dot(current_z, gripper_x) * gripper_x gripper_z = gripper_z / np.linalg.norm(gripper_z) gripper_y = np.cross(gripper_z, gripper_x) gripper_y = gripper_y / np.linalg.norm(gripper_y) gripper_z = np.cross(gripper_x, gripper_y) R_world_ee = np.column_stack((gripper_x, gripper_y, gripper_z)) q_world_ee = R.from_matrix(R_world_ee).as_quat(scalar_first=True) return q_world_ee def adding_waypoint(self, p_world_ee, q_world_ee, T_world_base): """ Adding a waypoint, also transform from wolrd frame to robot frame """ T_world_ee = tf_matrix_from_pose(p_world_ee, q_world_ee) T_base_ee = np.linalg.inv(T_world_base) @ T_world_ee p_base_ee, q_base_ee = pose_from_tf_matrix(T_base_ee) waypoint = np.concatenate((p_base_ee, q_base_ee)) return waypoint def is_feasible(self, th=10): return self.controller.num_plan_failed <= th def is_subtask_done(self, t_eps=1e-3, o_eps=5e-3): assert len(self.manip_list) != 0 p_base_ee_cur, q_base_ee_cur = self.controller.get_ee_pose() p_base_ee, q_base_ee, *_ = self.manip_list[0] diff_trans = np.linalg.norm(p_base_ee_cur - p_base_ee) diff_ori = 2 * np.arccos(min(abs(np.dot(q_base_ee_cur, q_base_ee)), 1.0)) pose_flag = np.logical_and( diff_trans < t_eps, diff_ori < o_eps, ) self.plan_flag = self.controller.num_last_cmd > 10 return np.logical_or(pose_flag, self.plan_flag) def is_done(self): if len(self.manip_list) == 0: return True if self.is_subtask_done(t_eps=self.skill_cfg.get("t_eps", 1e-3), o_eps=self.skill_cfg.get("o_eps", 5e-3)): self.manip_list.pop(0) return len(self.manip_list) == 0 def is_success(self): # Calculate the angle between the object's local y-axis and the world's z-axis T_world_obj = tf_matrix_from_pose(*self.pick_obj.get_local_pose()) obj_y_axis = T_world_obj[0:3, 1] # Extract the object's y-axis in world coordinates world_z_axis = np.array([0, 0, 1]) # World z-axis # Compute the angle between the two vectors dot_product = np.dot(obj_y_axis, world_z_axis) angle = np.arccos(np.clip(dot_product, -1.0, 1.0)) # Clip to handle numerical errors angle_degrees = np.degrees(angle) # Position ee_init_position = self.controller.T_world_ee_init[0:3, 3] # getting initial ee position obj_position = T_world_obj[0:3, 3] delta_y = obj_position[1] - ee_init_position[1] return angle_degrees < 90 and 0 < delta_y < 0.7