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.base import SawyerXYZEnv, _assert_task_is_set
  
  
  class SawyerSoccerEnv(SawyerXYZEnv):
      def __init__(self):
  
          goal_low = (-0.1, 0.8, 0.03)
          goal_high = (0.1, 0.9, 0.03)
          hand_low = (-0.5, 0.40, 0.05)
          hand_high = (0.5, 1, 0.5)
          obj_low = (-0.1, 0.6, 0.03)
          obj_high = (0.1, 0.7, 0.03)
  
          super().__init__(
              self.model_name,
              hand_low=hand_low,
              hand_high=hand_high,
          )
  
          self.init_config = {
              'obj_init_pos': np.array([0, 0.6, 0.03]),
              'obj_init_angle': 0.3,
              'hand_init_pos': np.array([0., .6, .2]),
          }
          self.goal = np.array([0., 0.9, 0.03])
          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_soccer.xml')
  
      @_assert_task_is_set
      def step(self, action):
          self.set_xyz_action(action[:3])
          self.do_simulation([action[-1], -action[-1]])
          # The marker seems to get reset every time you do a simulation
          self._set_goal_marker(self._state_goal)
          ob = self._get_obs()
          obs_dict = self._get_obs_dict()
          reward, reachDist, pushDist = self.compute_reward(action, obs_dict)
          self.curr_path_length += 1
  
          info = {'reachDist': reachDist, 'goalDist': pushDist, 'epRew' : reward, 'pickRew':None, 'success': float(pushDist <= 0.07)}
          info['goal'] = self.goal
  
          return ob, reward, False, info
  
      def _get_pos_objects(self):
          return self.data.get_geom_xpos('objGeom')
  
      def _set_goal_marker(self, goal):
          self.data.site_xpos[self.model.site_name2id('goal')] = (
              goal[:3]
          )
  
      def adjust_initObjPos(self, orig_init_pos):
          # This is to account for meshes for the geom and object are not aligned
          # If this is not done, the object could be initialized in an extreme position
          diff = self.get_body_com('obj')[:2] - self.data.get_geom_xpos('objGeom')[:2]
          adjustedPos = orig_init_pos[:2] + diff
  
          #The convention we follow is that body_com[2] is always 0, and geom_pos[2] is the object height
          return [adjustedPos[0], adjustedPos[1],self.data.get_geom_xpos('objGeom')[-1]]
  
      def reset_model(self):
          self._reset_hand()
          self._state_goal = self.goal.copy()
          self.obj_init_pos = self.adjust_initObjPos(self.init_config['obj_init_pos'])
          self.obj_init_angle = self.init_config['obj_init_angle']
          self.objHeight = self.data.get_geom_xpos('objGeom')[2]
  
          if self.random_init:
              goal_pos = self._get_state_rand_vec()
              self._state_goal = goal_pos[3:]
              while np.linalg.norm(goal_pos[:2] - self._state_goal[:2]) < 0.15:
                  goal_pos = self._get_state_rand_vec()
                  self._state_goal = goal_pos[3:]
              self.obj_init_pos = np.concatenate((goal_pos[:2], [self.obj_init_pos[-1]]))
              self.sim.model.body_pos[self.model.body_name2id('goal_whole')] = self._state_goal
  
          self._set_goal_marker(self._state_goal)
          self._set_obj_xyz(self.obj_init_pos)
          self.maxPushDist = np.linalg.norm(self.obj_init_pos[:2] - np.array(self._state_goal)[:2])
  
          return self._get_obs()
  
      def _reset_hand(self):
          for _ in range(10):
              self.data.set_mocap_pos('mocap', self.hand_init_pos)
              self.data.set_mocap_quat('mocap', np.array([1, 0, 1, 0]))
              self.do_simulation([-1,1], self.frame_skip)
  
          rightFinger, leftFinger = self.get_site_pos('rightEndEffector'), self.get_site_pos('leftEndEffector')
          self.init_fingerCOM  =  (rightFinger + leftFinger)/2
          self.reachCompleted = False
  
      def compute_reward(self, actions, obs):
          del actions
  
          obs = obs['state_observation']
  
          objPos = obs[3:6]
  
          rightFinger, leftFinger = self.get_site_pos('rightEndEffector'), self.get_site_pos('leftEndEffector')
          fingerCOM  =  (rightFinger + leftFinger)/2
  
          goal = self._state_goal
  
          c1 = 1000
          c2 = 0.01
          c3 = 0.001
          assert np.all(goal == self.get_site_pos('goal'))
          reachDist = np.linalg.norm(fingerCOM - objPos)
          pushDist = np.linalg.norm(objPos[:2] - goal[:2])
          reachRew = -reachDist
  
          def reachCompleted():
              return reachDist < 0.05
  
          self.reachCompleted = reachCompleted()
          if self.reachCompleted:
              pushRew = 1000*(self.maxPushDist - pushDist) + c1*(np.exp(-(pushDist**2)/c2) + np.exp(-(pushDist**2)/c3))
              pushRew = max(pushRew, 0)
          else:
              pushRew = 0
          reward = reachRew + pushRew
  
          return [reward, reachDist, pushDist]
  