コード例 #1
0
 def fn(l):
     # P(obs | s1=loc1, a=control_loc)
     if l == loc:
         return DDist({True: 1 - p_look_fn,
                       False: p_look_fn})
     return DDist({True: p_look_fp,
                   False: 1 - p_look_fp})
コード例 #2
0
 def decompose(self):
     if len(self.dist.support()) == 1:
         return self.dist.support()
     pose_dists = []
     for surface_name in self.surface_dist.support():
         dist = DDist({
             pose: self.discrete_prob(pose)
             for pose in self.poses_from_surface[surface_name]
         })
         weight = self.surface_prob(surface_name)
         pose_dists.append(SurfaceDist(self, weight, dist))
     return pose_dists
コード例 #3
0
 def update_dist(self, observation, obstacles=[], verbose=False):
     # cfree_dist.conditionOnVar(index=1, has_detection=True)
     if not BAYESIAN and (self.name in observation):
         # TODO: convert into a Multivariate Gaussian
         [detected_pose] = observation[self.name]
         return DeltaDist(detected_pose)
     if not self.world.cameras:
         return self.dist.copy()
     body = self.world.get_body(self.name)
     all_poses = self.dist.support()
     cfree_poses = all_poses
     #cfree_poses = compute_cfree(body, all_poses, obstacles)
     #cfree_dist = self.cfree_dist
     cfree_dist = DDist(
         {pose: self.dist.prob(pose)
          for pose in cfree_poses})
     # TODO: do these updates simultaneously for each object
     # TODO: check all camera poses
     [camera] = self.world.cameras.keys()
     info = self.world.cameras[camera]
     camera_pose = get_pose(info.body)
     detectable_poses = compute_detectable(cfree_poses, camera_pose)
     visible_poses = compute_visible(body,
                                     detectable_poses,
                                     camera_pose,
                                     draw=False)
     if verbose:
         print(
             'Total: {} | CFree: {} | Detectable: {} | Visible: {}'.format(
                 len(all_poses), len(cfree_poses), len(detectable_poses),
                 len(visible_poses)))
     assert set(visible_poses) <= set(detectable_poses)
     # obs_fn = get_observation_fn(surface)
     #wait_for_user()
     return self.bayesian_belief_update(cfree_dist,
                                        visible_poses,
                                        observation,
                                        verbose=verbose)
コード例 #4
0
ファイル: primitives.py プロジェクト: aiyi2099/pddlstream
 def fn(s):
     # P(obs | s1=loc1, a=control_loc)
     if s == surface:
         return DDist({True: 1 - p_look_fn, False: p_look_fn})
     return DDist({True: p_look_fp, False: 1 - p_look_fp})
コード例 #5
0
 def fn(pose):
     # P(detect | s in visible)
     # This could depend on the position as well
     if pose in visible:
         return DDist({pose.support: 1. - p_fn, None: p_fn})
     return DeltaDist(None)