content: import numpy as np
  import os
  import collections
  import matplotlib.pyplot as plt
  from dm_control import mujoco
  from dm_control.rl import control
  from dm_control.suite import base
  
  from constants import DT, XML_DIR, START_ARM_POSE
  from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN
  from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN
  from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN
  from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN
  
  import IPython
  e = IPython.embed
  
  BOX_POSE = [None] # to be changed from outside
  
  def make_sim_env(task_name):
      """
      Environment for simulated robot bi-manual manipulation, with joint position control
      Action space:      [left_arm_qpos (6),             # absolute joint position
                          left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
                          right_arm_qpos (6),            # absolute joint position
                          right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)
  
      Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
                                          left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
                                          right_arm_qpos (6),         # absolute joint position
                                          right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
                          "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
                                          left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
                                          right_arm_qvel (6),         # absolute joint velocity (rad)
                                          right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
                          "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
      """
      if 'sim_transfer_cube' in task_name:
          xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml')
          physics = mujoco.Physics.from_xml_path(xml_path)
          task = TransferCubeTask(random=False)
          env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
                                    n_sub_steps=None, flat_observation=False)
      elif 'sim_insertion' in task_name:
          xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml')
          physics = mujoco.Physics.from_xml_path(xml_path)
          task = InsertionTask(random=False)
          env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
                                    n_sub_steps=None, flat_observation=False)
      else:
          raise NotImplementedError
      return env
  
  class BimanualViperXTask(base.Task):
      def __init__(self, random=None):
          super().__init__(random=random)
  
      def before_step(self, action, physics):
          left_arm_action = action[:6]
          right_arm_action = action[7:7+6]
          normalized_left_gripper_action = action[6]
          normalized_right_gripper_action = action[7+6]
  
          left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action)
          right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action)
  
          full_left_gripper_action = [left_gripper_action, -left_gripper_action]
          full_right_gripper_action = [right_gripper_action, -right_gripper_action]
  
          env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action])
          super().before_step(env_action, physics)
          return
  
      def initialize_episode(self, physics):
          """Sets the state of the environment at the start of each episode."""
          super().initialize_episode(physics)
  
      @staticmethod
      def get_qpos(physics):
          qpos_raw = physics.data.qpos.copy()
          left_qpos_raw = qpos_raw[:8]
          right_qpos_raw = qpos_raw[8:16]
          left_arm_qpos = left_qpos_raw[:6]
          right_arm_qpos = right_qpos_raw[:6]
          left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])]
          right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])]
          return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
  
      @staticmethod
      def get_qvel(physics):
          qvel_raw = physics.data.qvel.copy()
          left_qvel_raw = qvel_raw[:8]
          right_qvel_raw = qvel_raw[8:16]
          left_arm_qvel = left_qvel_raw[:6]
          right_arm_qvel = right_qvel_raw[:6]
          left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])]
          right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])]
          return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
  
      @staticmethod
      def get_env_state(physics):
          raise NotImplementedError
  
      def get_observation(self, physics):
          obs = collections.OrderedDict()
          obs['qpos'] = self.get_qpos(physics)
          obs['qvel'] = self.get_qvel(physics)
          obs['env_state'] = self.get_env_state(physics)
          obs['images'] = dict()
          obs['images']['top'] = physics.render(height=480, width=640, camera_id='top')
          obs['images']['left_wrist'] = physics.render(height=480, width=640, camera_id='left_wrist')
          obs['images']['right_wrist'] = physics.render(height=480, width=640, camera_id='right_wrist')
          # obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle')
          # obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close')
  
          return obs
  
      def get_reward(self, physics):
          # return whether left gripper is holding the box
          raise NotImplementedError
  
  
  class TransferCubeTask(BimanualViperXTask):
      def __init__(self, random=None):
          super().__init__(random=random)
          self.max_reward = 4
  
      def initialize_episode(self, physics):
          """Sets the state of the environment at the start of each episode."""
          # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
          # reset qpos, control and box position
          with physics.reset_context():
              physics.named.data.qpos[:16] = START_ARM_POSE
              np.copyto(physics.data.ctrl, START_ARM_POSE)
              assert BOX_POSE[0] is not None
              physics.named.data.qpos[-7:] = BOX_POSE[0]
              # print(f"{BOX_POSE=}")
          super().initialize_episode(physics)
  
      @staticmethod
      def get_env_state(physics):
          env_state = physics.data.qpos.copy()[16:]
          return env_state
  
      def get_reward(self, physics):
          # return whether left gripper is holding the box
          all_contact_pairs = []
          for i_contact in range(physics.data.ncon):
              id_geom_1 = physics.data.contact[i_contact].geom1
              id_geom_2 = physics.data.contact[i_contact].geom2
              name_geom_1 = physics.model.id2name(id_geom_1, 'geom')
              name_geom_2 = physics.model.id2name(id_geom_2, 'geom')
              contact_pair = (name_geom_1, name_geom_2)
              all_contact_pairs.append(contact_pair)
  
          touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
          touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
          touch_table = ("red_box", "table") in all_contact_pairs
  
          reward = 0
          if touch_right_gripper:
              reward = 1
          if touch_right_gripper and not touch_table: # lifted
              reward = 2
          if touch_left_gripper: # attempted transfer
              reward = 3
          if touch_left_gripper and not touch_table: # successful transfer
              reward = 4
          return reward
  
  
  class InsertionTask(BimanualViperXTask):
      def __init__(self, random=None):
          super().__init__(random=random)
          self.max_reward = 4
  
      def initialize_episode(self, physics):
          """Sets the state of the environment at the start of each episode."""
          # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
          # reset qpos, control and box position
          with physics.reset_context():
              physics.named.data.qpos[:16] = START_ARM_POSE
              np.copyto(physics.data.ctrl, START_ARM_POSE)
              assert BOX_POSE[0] is not None
              physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects
              # print(f"{BOX_POSE=}")
          super().initialize_episode(physics)
  
      @staticmethod
      def get_env_state(physics):
          env_state = physics.data.qpos.copy()[16:]
          return env_state
  
      def get_reward(self, physics):
          # return whether peg touches the pin
          all_contact_pairs = []
          for i_contact in range(physics.data.ncon):
              id_geom_1 = physics.data.contact[i_contact].geom1
              id_geom_2 = physics.data.contact[i_contact].geom2
              name_geom_1 = physics.model.id2name(id_geom_1, 'geom')
              name_geom_2 = physics.model.id2name(id_geom_2, 'geom')
              contact_pair = (name_geom_1, name_geom_2)
              all_contact_pairs.append(contact_pair)
  
          touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
          touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
                               ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
                               ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
                               ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
  
          peg_touch_table = ("red_peg", "table") in all_contact_pairs
          socket_touch_table = ("socket-1", "table") in all_contact_pairs or \
                               ("socket-2", "table") in all_contact_pairs or \
                               ("socket-3", "table") in all_contact_pairs or \
                               ("socket-4", "table") in all_contact_pairs
          peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \
                             ("red_peg", "socket-2") in all_contact_pairs or \
                             ("red_peg", "socket-3") in all_contact_pairs or \
                             ("red_peg", "socket-4") in all_contact_pairs
          pin_touched = ("red_peg", "pin") in all_contact_pairs
  
          reward = 0
          if touch_left_gripper and touch_right_gripper: # touch both
              reward = 1
          if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both
              reward = 2
          if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching
              reward = 3
          if pin_touched: # successful insertion
              reward = 4
          return reward
  
  
  def get_action(master_bot_left, master_bot_right):
      action = np.zeros(14)
      # arm action
      action[:6] = master_bot_left.dxl.joint_states.position[:6]
      action[7:7+6] = master_bot_right.dxl.joint_states.position[:6]
      # gripper action
      left_gripper_pos = master_bot_left.dxl.joint_states.position[7]
      right_gripper_pos = master_bot_right.dxl.joint_states.position[7]
      normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos)
      normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos)
      action[6] = normalized_left_pos
      action[7+6] = normalized_right_pos
      return action
  
  def test_sim_teleop():
      """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """
      from interbotix_xs_modules.arm import InterbotixManipulatorXS
  
      BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0]
  
      # source of data
      master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
                                                robot_name=f'master_left', init_node=True)
      master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
                                                robot_name=f'master_right', init_node=False)
  
      # setup the environment
      env = make_sim_env('sim_transfer_cube')
      ts = env.reset()
      episode = [ts]
      # setup plotting
      ax = plt.subplot()
      plt_img = ax.imshow(ts.observation['images']['angle'])
      plt.ion()
  
      for t in range(1000):
          action = get_action(master_bot_left, master_bot_right)
          ts = env.step(action)
          episode.append(ts)
  
          plt_img.set_data(ts.observation['images']['angle'])
          plt.pause(0.02)
  
  
  if __name__ == '__main__':
      test_sim_teleop()
  
  