text: from collections import OrderedDict
  import random
  import numpy as np
  
  import robosuite.utils.transform_utils as T
  from robosuite.environments.robot_env import RobotEnv
  from robosuite.robots import SingleArm
  
  from robosuite.models.arenas import PegsArena
  from robosuite.models.objects import SquareNutObject, RoundNutObject
  from robosuite.models.tasks import ManipulationTask, SequentialCompositeSampler
  
  
  class NutAssembly(RobotEnv):
      """
      This class corresponds to the nut assembly task for a single robot arm.
  
      Args:
          robots (str or list of str): Specification for specific robot arm(s) to be instantiated within this env
              (e.g: "Sawyer" would generate one arm; ["Panda", "Panda", "Sawyer"] would generate three robot arms)
              Note: Must be a single single-arm robot!
  
          controller_configs (str or list of dict): If set, contains relevant controller parameters for creating a
              custom controller. Else, uses the default controller for this specific task. Should either be single
              dict if same controller is to be used for all robots or else it should be a list of the same length as
              "robots" param
  
          gripper_types (str or list of str): type of gripper, used to instantiate
              gripper models from gripper factory. Default is "default", which is the default grippers(s) associated
              with the robot(s) the 'robots' specification. None removes the gripper, and any other (valid) model
              overrides the default gripper. Should either be single str if same gripper type is to be used for all
              robots or else it should be a list of the same length as "robots" param
  
          gripper_visualizations (bool or list of bool): True if using gripper visualization.
              Useful for teleoperation. Should either be single bool if gripper visualization is to be used for all
              robots or else it should be a list of the same length as "robots" param
  
          initialization_noise (dict or list of dict): Dict containing the initialization noise parameters.
              The expected keys and corresponding value types are specified below:
  
              :`'magnitude'`: The scale factor of uni-variate random noise applied to each of a robot's given initial
                  joint positions. Setting this value to `None` or 0.0 results in no noise being applied.
                  If "gaussian" type of noise is applied then this magnitude scales the standard deviation applied,
                  If "uniform" type of noise is applied then this magnitude sets the bounds of the sampling range
              :`'type'`: Type of noise to apply. Can either specify "gaussian" or "uniform"
  
              Should either be single dict if same noise value is to be used for all robots or else it should be a
              list of the same length as "robots" param
  
              :Note: Specifying "default" will automatically use the default noise settings.
                  Specifying None will automatically create the required dict with "magnitude" set to 0.0.
  
          table_full_size (3-tuple): x, y, and z dimensions of the table.
  
          table_friction (3-tuple): the three mujoco friction parameters for
              the table.
  
          use_camera_obs (bool): if True, every observation includes rendered image(s)
  
          use_object_obs (bool): if True, include object (cube) information in
              the observation.
  
          reward_scale (None or float): Scales the normalized reward function by the amount specified.
              If None, environment reward remains unnormalized
  
          reward_shaping (bool): if True, use dense rewards.
  
          placement_initializer (ObjectPositionSampler instance): if provided, will
              be used to place objects on every reset, else a UniformRandomSampler
              is used by default.
  
          single_object_mode (int): specifies which version of the task to do. Note that
              the observations change accordingly.
  
              :`0`: corresponds to the full task with both types of nuts.
  
              :`1`: corresponds to an easier task with only one type of nut initialized
                 on the table with every reset. The type is randomized on every reset.
  
              :`2`: corresponds to an easier task with only one type of nut initialized
                 on the table with every reset. The type is kept constant and will not
                 change between resets.
  
          nut_type (string): if provided, should be either "round" or "square". Determines
              which type of nut (round or square) will be spawned on every environment
              reset. Only used if @single_object_mode is 2.
  
          use_indicator_object (bool): if True, sets up an indicator object that
              is useful for debugging.
  
          has_renderer (bool): If true, render the simulation state in
              a viewer instead of headless mode.
  
          has_offscreen_renderer (bool): True if using off-screen rendering
  
          render_camera (str): Name of camera to render if `has_renderer` is True. Setting this value to 'None'
              will result in the default angle being applied, which is useful as it can be dragged / panned by
              the user using the mouse
  
          render_collision_mesh (bool): True if rendering collision meshes in camera. False otherwise.
  
          render_visual_mesh (bool): True if rendering visual meshes in camera. False otherwise.
  
          control_freq (float): how many control signals to receive in every second. This sets the amount of
              simulation time that passes between every action input.
  
          horizon (int): Every episode lasts for exactly @horizon timesteps.
  
          ignore_done (bool): True if never terminating the environment (ignore @horizon).
  
          hard_reset (bool): If True, re-loads model, sim, and render object upon a reset call, else,
              only calls sim.reset and resets all robosuite-internal variables
  
          camera_names (str or list of str): name of camera to be rendered. Should either be single str if
              same name is to be used for all cameras' rendering or else it should be a list of cameras to render.
  
              :Note: At least one camera must be specified if @use_camera_obs is True.
  
              :Note: To render all robots' cameras of a certain type (e.g.: "robotview" or "eye_in_hand"), use the
                  convention "all-{name}" (e.g.: "all-robotview") to automatically render all camera images from each
                  robot's camera list).
  
          camera_heights (int or list of int): height of camera frame. Should either be single int if
              same height is to be used for all cameras' frames or else it should be a list of the same length as
              "camera names" param.
  
          camera_widths (int or list of int): width of camera frame. Should either be single int if
              same width is to be used for all cameras' frames or else it should be a list of the same length as
              "camera names" param.
  
          camera_depths (bool or list of bool): True if rendering RGB-D, and RGB otherwise. Should either be single
              bool if same depth setting is to be used for all cameras or else it should be a list of the same length as
              "camera names" param.
  
      Raises:
          AssertionError: [Invalid nut type specified]
          AssertionError: [Invalid number of robots specified]
      """
  
      def __init__(
          self,
          robots,
          controller_configs=None,
          gripper_types="default",
          gripper_visualizations=False,
          initialization_noise="default",
          table_full_size=(0.8, 0.8, 0.05),
          table_friction=(1, 0.005, 0.0001),
          use_camera_obs=True,
          use_object_obs=True,
          reward_scale=1.0,
          reward_shaping=False,
          placement_initializer=None,
          single_object_mode=0,
          nut_type=None,
          use_indicator_object=False,
          has_renderer=False,
          has_offscreen_renderer=True,
          render_camera="frontview",
          render_collision_mesh=False,
          render_visual_mesh=True,
          control_freq=10,
          horizon=1000,
          ignore_done=False,
          hard_reset=True,
          camera_names="agentview",
          camera_heights=256,
          camera_widths=256,
          camera_depths=False,
      ):
          # First, verify that only one robot is being inputted
          self._check_robot_configuration(robots)
  
          # task settings
          self.single_object_mode = single_object_mode
          self.nut_to_id = {"square": 0, "round": 1}
          if nut_type is not None:
              assert (
                      nut_type in self.nut_to_id.keys()
              ), "invalid @nut_type argument - choose one of {}".format(
                  list(self.nut_to_id.keys())
              )
              self.nut_id = self.nut_to_id[nut_type]  # use for convenient indexing
          self.obj_to_use = None
  
          # settings for table top
          self.table_full_size = table_full_size
          self.table_friction = table_friction
  
          # reward configuration
          self.reward_scale = reward_scale
          self.reward_shaping = reward_shaping
  
          # whether to use ground-truth object states
          self.use_object_obs = use_object_obs
  
          # object placement initializer
          if placement_initializer:
              self.placement_initializer = placement_initializer
          else:
              # treat sampling of each type of nut differently since we require different
              # sampling ranges for each
              self.placement_initializer = SequentialCompositeSampler()
              self.placement_initializer.sample_on_top(
                  "SquareNut0",
                  surface_name="table",
                  x_range=[-0.115, -0.11],
                  y_range=[0.11, 0.225],
                  rotation=None,
                  rotation_axis='z',
                  z_offset=0.02,
                  ensure_object_boundary_in_range=False,
              )
              self.placement_initializer.sample_on_top(
                  "RoundNut0",
                  surface_name="table",
                  x_range=[-0.115, -0.11],
                  y_range=[-0.225, -0.11],
                  rotation=None,
                  rotation_axis='z',
                  z_offset=0.02,
                  ensure_object_boundary_in_range=False,
              )
  
          super().__init__(
              robots=robots,
              controller_configs=controller_configs,
              gripper_types=gripper_types,
              gripper_visualizations=gripper_visualizations,
              initialization_noise=initialization_noise,
              use_camera_obs=use_camera_obs,
              use_indicator_object=use_indicator_object,
              has_renderer=has_renderer,
              has_offscreen_renderer=has_offscreen_renderer,
              render_camera=render_camera,
              render_collision_mesh=render_collision_mesh,
              render_visual_mesh=render_visual_mesh,
              control_freq=control_freq,
              horizon=horizon,
              ignore_done=ignore_done,
              hard_reset=hard_reset,
              camera_names=camera_names,
              camera_heights=camera_heights,
              camera_widths=camera_widths,
              camera_depths=camera_depths,
          )
  
      def reward(self, action=None):
          """
          Reward function for the task.
  
          Sparse un-normalized reward:
  
            - a discrete reward of 1.0 per nut if it is placed around its correct peg
  
          Un-normalized components if using reward shaping, where the maximum is returned if not solved:
  
            - Reaching: in [0, 0.1], proportional to the distance between the gripper and the closest nut
            - Grasping: in {0, 0.35}, nonzero if the gripper is grasping a nut
            - Lifting: in {0, [0.35, 0.5]}, nonzero only if nut is grasped; proportional to lifting height
            - Hovering: in {0, [0.5, 0.7]}, nonzero only if nut is lifted; proportional to distance from nut to peg
  
          Note that a successfully completed task (nut around peg) will return 1.0 per nut irregardless of whether the
          environment is using sparse or shaped rewards
  
          Note that the final reward is normalized and scaled by reward_scale / 2.0 (or 1.0 if only a single nut is
          being used) as well so that the max score is equal to reward_scale
  
          Args:
              action (np.array): [NOT USED]
  
          Returns:
              float: reward value
          """
          # compute sparse rewards
          self._check_success()
          reward = np.sum(self.objects_on_pegs)
  
          # add in shaped rewards
          if self.reward_shaping:
              staged_rewards = self.staged_rewards()
              reward += max(staged_rewards)
          if self.reward_scale is not None:
              reward *= self.reward_scale
              if self.single_object_mode == 0:
                  reward /= 2.0
          return reward
  
      def staged_rewards(self):
          """
          Calculates staged rewards based on current physical states.
          Stages consist of reaching, grasping, lifting, and hovering.
  
          Returns:
              4-tuple:
  
                  - (float) reaching reward
                  - (float) grasping reward
                  - (float) lifting reward
                  - (float) hovering reward
          """
  
          reach_mult = 0.1
          grasp_mult = 0.35
          lift_mult = 0.5
          hover_mult = 0.7
  
          # filter out objects that are already on the correct pegs
          names_to_reach = []
          objs_to_reach = []
          geoms_to_grasp = []
          geoms_by_array = []
  
          for i in range(len(self.ob_inits)):
              if self.objects_on_pegs[i]:
                  continue
              obj_str = str(self.item_names[i]) + "0"
              names_to_reach.append(obj_str)
              objs_to_reach.append(self.obj_body_id[obj_str])
              geoms_to_grasp.extend(self.obj_geom_id[obj_str])
              geoms_by_array.append(self.obj_geom_id[obj_str])
  
          ### reaching reward governed by distance to closest object ###
          r_reach = 0.
          if len(objs_to_reach):
              # reaching reward via minimum distance to the handles of the objects (the last geom of each nut)
              geom_ids = [elem[-1] for elem in geoms_by_array]
              target_geom_pos = self.sim.data.geom_xpos[geom_ids]
              gripper_site_pos = self.sim.data.site_xpos[self.robots[0].eef_site_id]
              dists = np.linalg.norm(
                  target_geom_pos - gripper_site_pos.reshape(1, -1), axis=1
              )
              r_reach = (1 - np.tanh(10.0 * min(dists))) * reach_mult
  
          ### grasping reward for touching any objects of interest ###
          touch_left_finger = False
          touch_right_finger = False
          for i in range(self.sim.data.ncon):
              c = self.sim.data.contact[i]
              if c.geom1 in geoms_to_grasp:
                  if c.geom2 in self.l_finger_geom_ids:
                      touch_left_finger = True
                  if c.geom2 in self.r_finger_geom_ids:
                      touch_right_finger = True
              elif c.geom2 in geoms_to_grasp:
                  if c.geom1 in self.l_finger_geom_ids:
                      touch_left_finger = True
                  if c.geom1 in self.r_finger_geom_ids:
                      touch_right_finger = True
          has_grasp = touch_left_finger and touch_right_finger
          r_grasp = int(has_grasp) * grasp_mult
  
          ### lifting reward for picking up an object ###
          r_lift = 0.
          table_pos = np.array(self.sim.data.body_xpos[self.table_body_id])
          if len(objs_to_reach) and r_grasp > 0.:
              z_target = table_pos[2] + 0.2
              object_z_locs = self.sim.data.body_xpos[objs_to_reach][:, 2]
              z_dists = np.maximum(z_target - object_z_locs, 0.)
              r_lift = grasp_mult + (1 - np.tanh(15.0 * min(z_dists))) * (
                      lift_mult - grasp_mult
              )
  
          ### hover reward for getting object above peg ###
          r_hover = 0.
          if len(objs_to_reach):
              r_hovers = np.zeros(len(objs_to_reach))
              for i in range(len(objs_to_reach)):
                  if names_to_reach[i].startswith(self.item_names[0]):
                      peg_pos = np.array(self.sim.data.body_xpos[self.peg1_body_id])[:2]
                  elif names_to_reach[i].startswith(self.item_names[1]):
                      peg_pos = np.array(self.sim.data.body_xpos[self.peg2_body_id])[:2]
                  else:
                      raise Exception(
                          "Got invalid object to reach: {}".format(names_to_reach[i])
                      )
                  ob_xy = self.sim.data.body_xpos[objs_to_reach[i]][:2]
                  dist = np.linalg.norm(peg_pos - ob_xy)
                  r_hovers[i] = r_lift + (1 - np.tanh(10.0 * dist)) * (
                          hover_mult - lift_mult
                  )
              r_hover = np.max(r_hovers)
  
          return r_reach, r_grasp, r_lift, r_hover
  
      def on_peg(self, obj_pos, peg_id):
  
          if peg_id == 0:
              peg_pos = np.array(self.sim.data.body_xpos[self.peg1_body_id])
          else:
              peg_pos = np.array(self.sim.data.body_xpos[self.peg2_body_id])
          res = False
          if (
                  abs(obj_pos[0] - peg_pos[0]) < 0.03
                  and abs(obj_pos[1] - peg_pos[1]) < 0.03
                  and obj_pos[2] < self.mujoco_arena.table_offset[2] + 0.05
          ):
              res = True
          return res
  
      def clear_objects(self, obj):
          """
          Clears objects without the name @obj out of the task space. This is useful
          for supporting task modes with single types of objects, as in
          @self.single_object_mode without changing the model definition.
  
          Args:
              obj (str): Name of object to keep in the task space
          """
          for obj_name, obj_mjcf in self.mujoco_objects.items():
              if obj_name == obj:
                  continue
              else:
                  sim_state = self.sim.get_state()
                  # print(self.sim.model.get_joint_qpos_addr(obj_name))
                  sim_state.qpos[self.sim.model.get_joint_qpos_addr(obj_name + "_jnt0")[0]] = 10
                  self.sim.set_state(sim_state)
                  self.sim.forward()
  
      def _load_model(self):
          """
          Loads an xml model, puts it in self.model
          """
          super()._load_model()
  
          # Verify the correct robot has been loaded
          assert isinstance(self.robots[0], SingleArm), \
              "Error: Expected one single-armed robot! Got {} type instead.".format(type(self.robots[0]))
  
          # Adjust base pose accordingly
          xpos = self.robots[0].robot_model.base_xpos_offset["table"](self.table_full_size[0])
          self.robots[0].robot_model.set_base_xpos(xpos)
  
          # load model for table top workspace
          self.mujoco_arena = PegsArena(
              table_full_size=self.table_full_size,
              table_friction=self.table_friction,
              table_offset=(0, 0, 0.82)
          )
          if self.use_indicator_object:
              self.mujoco_arena.add_pos_indicator()
  
          # Arena always gets set to zero origin
          self.mujoco_arena.set_origin([0, 0, 0])
  
          # define mujoco objects
          self.ob_inits = [SquareNutObject, RoundNutObject]
          self.item_names = ["SquareNut", "RoundNut"]
          self.item_names_org = list(self.item_names)
          self.obj_to_use = (self.item_names[1] + "{}").format(0)
          self.ngeoms = [5, 9]
  
          lst = []
          for i in range(len(self.ob_inits)):
              ob_name = (self.item_names[i] + "0")
              ob = self.ob_inits[i](
                  name=ob_name,
                  joints=[dict(type="free", damping="0.0005")], # damp the free joint for each object
              )
              lst.append((ob_name, ob))
  
          self.mujoco_objects = OrderedDict(lst)
          self.n_objects = len(self.mujoco_objects)
  
          # task includes arena, robot, and objects of interest
          self.model = ManipulationTask(
              mujoco_arena=self.mujoco_arena, 
              mujoco_robots=[robot.robot_model for robot in self.robots], 
              mujoco_objects=self.mujoco_objects, 
              visual_objects=None, 
              initializer=self.placement_initializer,
          )
  
          # set positions of objects
          self.model.place_objects()
  
      def _get_reference(self):
          """
          Sets up references to important components. A reference is typically an
          index or a list of indices that point to the corresponding elements
          in a flatten array, which is how MuJoCo stores physical simulation data.
          """
          super()._get_reference()
  
          # Additional object references from this env
          self.obj_body_id = {}
          self.obj_geom_id = {}
  
          self.table_body_id = self.sim.model.body_name2id("table")
          self.peg1_body_id = self.sim.model.body_name2id("peg1")
          self.peg2_body_id = self.sim.model.body_name2id("peg2")
  
          for i in range(len(self.ob_inits)):
              obj_str = str(self.item_names[i]) + "0"
              self.obj_body_id[obj_str] = self.sim.model.body_name2id(obj_str)
              geom_ids = []
              for j in range(self.ngeoms[i]):
                  geom_ids.append(self.sim.model.geom_name2id(obj_str + "-{}".format(j)))
              self.obj_geom_id[obj_str] = geom_ids
  
          # information of objects
          self.object_names = list(self.mujoco_objects.keys())
          self.object_site_ids = [
              self.sim.model.site_name2id(ob_name) for ob_name in self.object_names
          ]
  
          # id of grippers for contact checking
          self.l_finger_geom_ids = [
              self.sim.model.geom_name2id(x) for x in self.robots[0].gripper.important_geoms["left_finger"]
          ]
          self.r_finger_geom_ids = [
              self.sim.model.geom_name2id(x) for x in self.robots[0].gripper.important_geoms["right_finger"]
          ]
          # self.sim.data.contact # list, geom1, geom2
          self.collision_check_geom_names = self.sim.model._geom_name2id.keys()
          self.collision_check_geom_ids = [
              self.sim.model._geom_name2id[k] for k in self.collision_check_geom_names
          ]
  
          # keep track of which objects are on their corresponding pegs
          self.objects_on_pegs = np.zeros(len(self.ob_inits))
  
      def _reset_internal(self):
          """
          Resets simulation internal configurations.
          """
          super()._reset_internal()
  
          # Reset all object positions using initializer sampler if we're not directly loading from an xml
          if not self.deterministic_reset:
  
              # Sample from the placement initializer for all objects
              obj_pos, obj_quat = self.model.place_objects()
  
              # Loop through all objects and reset their positions
              for i, (obj_name, _) in enumerate(self.mujoco_objects.items()):
                  self.sim.data.set_joint_qpos(obj_name + "_jnt0", np.concatenate([np.array(obj_pos[i]), np.array(obj_quat[i])]))
  
          # information of objects
          self.object_names = list(self.mujoco_objects.keys())
          self.object_site_ids = [
              self.sim.model.site_name2id(ob_name) for ob_name in self.object_names
          ]
  
          # Move objects out of the scene depending on the mode
          if self.single_object_mode == 1:
              self.obj_to_use = (random.choice(self.item_names) + "{}").format(0)
              self.clear_objects(self.obj_to_use)
          elif self.single_object_mode == 2:
              self.obj_to_use = (self.item_names[self.nut_id] + "{}").format(0)
              self.clear_objects(self.obj_to_use)
  
      def _get_observation(self):
          """
          Returns an OrderedDict containing observations [(name_string, np.array), ...].
  
          Important keys:
  
              `'robot-state'`: contains robot-centric information.
  
              `'object-state'`: requires @self.use_object_obs to be True. Contains object-centric information.
  
              `'image'`: requires @self.use_camera_obs to be True. Contains a rendered frame from the simulation.
  
              `'depth'`: requires @self.use_camera_obs and @self.camera_depth to be True.
              Contains a rendered depth map from the simulation
  
          Returns:
              OrderedDict: Observations from the environment
          """
          di = super()._get_observation()
  
          # low-level object information
          if self.use_object_obs:
              # Get robot prefix
              pr = self.robots[0].robot_model.naming_prefix
  
              # remember the keys to collect into object info
              object_state_keys = []
  
              # for conversion to relative gripper frame
              gripper_pose = T.pose2mat((di[pr + "eef_pos"], di[pr + "eef_quat"]))
              world_pose_in_gripper = T.pose_inv(gripper_pose)
  
              for i in range(len(self.item_names_org)):
  
                  if self.single_object_mode == 2 and self.nut_id != i:
                      # skip observations
                      continue
  
                  obj_str = str(self.item_names_org[i]) + "0"
                  obj_pos = np.array(self.sim.data.body_xpos[self.obj_body_id[obj_str]])
                  obj_quat = T.convert_quat(
                      self.sim.data.body_xquat[self.obj_body_id[obj_str]], to="xyzw"
                  )
                  di["{}_pos".format(obj_str)] = obj_pos
                  di["{}_quat".format(obj_str)] = obj_quat
  
                  object_pose = T.pose2mat((obj_pos, obj_quat))
                  rel_pose = T.pose_in_A_to_pose_in_B(object_pose, world_pose_in_gripper)
                  rel_pos, rel_quat = T.mat2pose(rel_pose)
                  di["{}_to_{}eef_pos".format(obj_str, pr)] = rel_pos
                  di["{}_to_{}eef_quat".format(obj_str, pr)] = rel_quat
  
                  object_state_keys.append("{}_pos".format(obj_str))
                  object_state_keys.append("{}_quat".format(obj_str))
                  object_state_keys.append("{}_to_{}eef_pos".format(obj_str, pr))
                  object_state_keys.append("{}_to_{}eef_quat".format(obj_str, pr))
  
              if self.single_object_mode == 1:
                  # zero out other objs
                  for obj_str, obj_mjcf in self.mujoco_objects.items():
                      if obj_str == self.obj_to_use:
                          continue
                      else:
                          di["{}_pos".format(obj_str)] *= 0.0
                          di["{}_quat".format(obj_str)] *= 0.0
                          di["{}_to_{}eef_pos".format(obj_str, pr)] *= 0.0
                          di["{}_to_{}eef_quat".format(obj_str, pr)] *= 0.0
  
              di["object-state"] = np.concatenate([di[k] for k in object_state_keys])
  
          return di
  
      def _check_success(self):
          """
          Check if all nuts have been successfully placed around their corresponding pegs.
  
          Returns:
              bool: True if all nuts are placed correctly
          """
          # remember objects that are on the correct pegs
          gripper_site_pos = self.sim.data.site_xpos[self.robots[0].eef_site_id]
          for i in range(len(self.ob_inits)):
              obj_str = str(self.item_names[i]) + "0"
              obj_pos = self.sim.data.body_xpos[self.obj_body_id[obj_str]]
              dist = np.linalg.norm(gripper_site_pos - obj_pos)
              r_reach = 1 - np.tanh(10.0 * dist)
              self.objects_on_pegs[i] = int(self.on_peg(obj_pos, i) and r_reach < 0.6)
  
          if self.single_object_mode > 0:
              return np.sum(self.objects_on_pegs) > 0  # need one object on peg
  
          # returns True if all objects are on correct pegs
          return np.sum(self.objects_on_pegs) == len(self.ob_inits)
  
      def _visualization(self):
          """
          Do any needed visualization here. Overrides superclass implementations.
          """
  
          # color the gripper site appropriately based on distance to cube
          if self.robots[0].gripper_visualization:
              # find closest object
              square_dist = lambda x: np.sum(
                  np.square(x - self.sim.data.get_site_xpos(self.robots[0].gripper.visualization_sites["grip_site"]))
              )
              dists = np.array(list(map(square_dist, self.sim.data.site_xpos)))
              dists[self.robots[0].eef_site_id] = np.inf  # make sure we don't pick the same site
              dists[self.robots[0].eef_cylinder_id] = np.inf
              ob_dists = dists[
                  self.object_site_ids
              ]  # filter out object sites we care about
              min_dist = np.min(ob_dists)
              ob_id = np.argmin(ob_dists)
  
              # set RGBA for the EEF site here
              max_dist = 0.1
              scaled = (1.0 - min(min_dist / max_dist, 1.)) ** 15
              rgba = np.zeros(4)
              rgba[0] = 1 - scaled
              rgba[1] = scaled
              rgba[3] = 0.5
  
              self.sim.model.site_rgba[self.robots[0].eef_site_id] = rgba
  
      def _check_robot_configuration(self, robots):
          """
          Sanity check to make sure the inputted robots and configuration is acceptable
  
          Args:
              robots (str or list of str): Robots to instantiate within this env
          """
          if type(robots) is list:
              assert len(robots) == 1, "Error: Only one robot should be inputted for this task!"
  
  
  class NutAssemblySingle(NutAssembly):
      """
      Easier version of task - place either one round nut or one square nut into its peg.
      """
  
      def __init__(self, **kwargs):
          assert "single_object_mode" not in kwargs, "invalid set of arguments"
          super().__init__(single_object_mode=1, **kwargs)
  
  
  class NutAssemblySquare(NutAssembly):
      """
      Easier version of task - place one square nut into its peg.
      """
  
      def __init__(self, **kwargs):
          assert (
                  "single_object_mode" not in kwargs and "nut_type" not in kwargs
          ), "invalid set of arguments"
          super().__init__(single_object_mode=2, nut_type="square", **kwargs)
  
  
  class NutAssemblyRound(NutAssembly):
      """
      Easier version of task - place one round nut into its peg.
      """
  
      def __init__(self, **kwargs):
          assert (
                  "single_object_mode" not in kwargs and "nut_type" not in kwargs
          ), "invalid set of arguments"
          super().__init__(single_object_mode=2, nut_type="round", **kwargs)
  