text: import numpy as np
  from gym.spaces import Box
  
  from metaworld.envs.env_util import get_asset_full_path
  from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set
  
  
  class SawyerPlateSlideEnvV2(SawyerXYZEnv):
  
      def __init__(self):
  
          goal_low = (-0.1, 0.85, 0.)
          goal_high = (0.1, 0.9, 0.)
          hand_low = (-0.5, 0.40, 0.05)
          hand_high = (0.5, 1, 0.5)
          obj_low = (0., 0.6, 0.)
          obj_high = (0., 0.6, 0.)
  
          super().__init__(
              self.model_name,
              hand_low=hand_low,
              hand_high=hand_high,
          )
  
          self.init_config = {
              'obj_init_angle': 0.3,
              'obj_init_pos': np.array([0., 0.6, 0.], dtype=np.float32),
              'hand_init_pos': np.array((0, 0.6, 0.2), dtype=np.float32),
          }
          self.goal = np.array([0., 0.85, 0.02])
          self.obj_init_pos = self.init_config['obj_init_pos']
          self.obj_init_angle = self.init_config['obj_init_angle']
          self.hand_init_pos = self.init_config['hand_init_pos']
  
          self.max_path_length = 150
  
          self._random_reset_space = Box(
              np.hstack((obj_low, goal_low)),
              np.hstack((obj_high, goal_high)),
          )
          self.goal_space = Box(np.array(goal_low), np.array(goal_high))
  
      @property
      def model_name(self):
          return get_asset_full_path('sawyer_xyz/sawyer_plate_slide.xml', True)
  
      @_assert_task_is_set
      def step(self, action):
          ob = super().step(action)
          reward, reachDist, pullDist = self.compute_reward(action, ob)
          self.curr_path_length += 1
  
          info = {
              'reachDist': reachDist,
              'goalDist': pullDist,
              'epRew': reward,
              'pickRew': None,
              'success': float(pullDist <= 0.08)
          }
  
          return ob, reward, False, info
  
      def _get_pos_objects(self):
          return self.data.get_geom_xpos('puck')
  
      def _set_obj_xyz(self, pos):
          qpos = self.data.qpos.flat.copy()
          qvel = self.data.qvel.flat.copy()
          qpos[9:11] = pos
          self.set_state(qpos, qvel)
  
      def reset_model(self):
          self._reset_hand()
  
          self.obj_init_pos = self.init_config['obj_init_pos']
          self._target_pos = self.goal.copy()
  
          if self.random_init:
              rand_vec = self._get_state_rand_vec()
              self.obj_init_pos = rand_vec[:3]
              self._target_pos = rand_vec[3:]
  
          self.sim.model.body_pos[
              self.model.body_name2id('puck_goal')] = self._target_pos
          self._set_obj_xyz(np.zeros(2))
  
          self.objHeight = self.data.get_geom_xpos('puck')[2]
          self.maxDist = np.linalg.norm(
              self.obj_init_pos[:-1] - self._target_pos[:-1])
          self.target_reward = 1000 * self.maxDist + 1000 * 2
  
          return self._get_obs()
  
      def _reset_hand(self):
          super()._reset_hand()
  
      def compute_reward(self, actions, obs):
          del actions
  
          objPos = obs[3:6]
  
          rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
          fingerCOM  =  (rightFinger + leftFinger)/2
  
          pullGoal = self._target_pos
  
          reachDist = np.linalg.norm(objPos - fingerCOM)
  
          pullDist = np.linalg.norm(objPos[:-1] - pullGoal[:-1])
  
          c1 = 1000
          c2 = 0.01
          c3 = 0.001
          if reachDist < 0.05:
              pullRew = 1000*(self.maxDist - pullDist) + c1*(np.exp(-(pullDist**2)/c2) + np.exp(-(pullDist**2)/c3))
              pullRew = max(pullRew, 0)
          else:
              pullRew = 0
          reward = -reachDist + pullRew
  
          return [reward, reachDist, pullDist]
  