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