diff --git a/vmas/scenarios/transport.py b/vmas/scenarios/transport.py
index a264a744..b781f784 100644
--- a/vmas/scenarios/transport.py
+++ b/vmas/scenarios/transport.py
@@ -81,8 +81,8 @@ def make_world(self, batch_dim: int, device: torch.device, **kwargs):
         self.package_mass = kwargs.get("package_mass", 3)
 
         # partial obs
-        self.partial_observations = kwargs.get("partial_observations", False)
-        self.package_observation_radius = kwargs.get("package_observation_radius", 0.35)
+        self.partial_observations = kwargs.get("partial_observations", True)
+        self.package_observation_dist = kwargs.get("package_observation_dist", 0.35)
 
         # realism
         self.linear_friction = kwargs.get("linear_friction", 0.01)
@@ -131,6 +131,7 @@ def make_world(self, batch_dim: int, device: torch.device, **kwargs):
 
         # Add agents
         capabilities = [] # save capabilities for relative capabilities later
+        self.observation_sensors = [] # for partial observability
         for i in range(n_agents):
             max_linear_vel = self.default_agent_max_linear_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
             max_angular_vel = self.default_agent_max_angular_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
@@ -152,6 +153,20 @@ def make_world(self, batch_dim: int, device: torch.device, **kwargs):
 
             world.add_agent(agent)
 
+            # add the observation sensor if partial observability is turned on.
+            if self.partial_observations:
+                self.observation_sensors.append(
+                    Landmark(
+                        name=f'obs_sensor_agent_{i}',
+                        collide=False,
+                        shape=Sphere(radius=self.package_observation_dist+radius),
+                        color=(0.827, 0.827, 0.827, 0.65),
+                        movable=False,
+                    )
+                )
+                world.add_landmark(self.observation_sensors[-1])
+
+
         self.capabilities = torch.tensor(capabilities)
 
         # Add landmarks
@@ -191,7 +206,7 @@ def reset_world_at(self, env_index: int = None):
         # only do this during batched resets!
         if not env_index:        
             capabilities = [] # save capabilities for relative capabilities later
-            for agent in self.world.agents:
+            for i, agent in enumerate(self.world.agents):
                 max_linear_vel = self.default_agent_max_linear_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
                 max_angular_vel = self.default_agent_max_angular_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
                 radius = self.default_agent_radius * random.uniform(self.capability_mult_min, self.capability_mult_max)
@@ -203,6 +218,12 @@ def reset_world_at(self, env_index: int = None):
                 agent.shape=Sphere(radius)
                 agent.mass=mass
 
+                # spawn the sensor radius for each agent
+                if self.partial_observations:
+                    self.observation_sensors[i].set_pos(self.world.agents[i].state.pos, env_index)
+                    self.observation_sensors[i].shape = Sphere(self.package_observation_dist+radius)
+        
+
             self.capabilities = torch.tensor(capabilities)
 
         # spawn goal at origin
@@ -256,7 +277,7 @@ def reset_world_at(self, env_index: int = None):
             ),
             occupied_positions=package_occupied_pos,
         )
-        
+
         self.package_starting_dists = []
         self.og_package_positions = []
         for i, package in enumerate(self.packages):
@@ -444,12 +465,17 @@ def partial_observation(self, agent: Agent):
          # get positions of all entities in this agent's reference frame
         package_obs = []
         out_of_obs_val = -0.0001 # default value used for out-of-observation data in the observation vector
+        
+        # spawn the sensor radius for each agent
+        for i, agent_i_sensor in enumerate(self.observation_sensors):
+            agent_i_sensor.set_pos(self.world.agents[i].state.pos, None)
+
         for i, package in enumerate(self.packages):
             # box starting position and goal position alway part of the observation
             package_obs.append(self.og_package_positions[i])
             package_obs.append(package.on_goal.unsqueeze(-1))
             
-            mask = (torch.linalg.vector_norm(package.state.pos - agent.state.pos, dim=-1) < self.package_observation_radius)
+            mask = self.world.is_overlapping(self.observation_sensors[i], package)
             pkg_state_vec = package.state.pos.clone()
             pkg_rot_vec = package.state.rot.clone()
             pkg_vel_vec = package.state.vel.clone()
@@ -606,15 +632,6 @@ def extra_render(self, env_index: int = 0) -> "List[Geom]":
         geoms: List[Geom] = []
         if not self.partial_observations:
             return geoms
-
-        for i, agent in enumerate(self.world.agents):
-
-            obs_circle = rendering.make_circle(self.package_observation_radius, filled=True)
-            xform = rendering.Transform()
-            xform.set_translation(*agent.state.pos[env_index])
-            obs_circle.add_attr(xform)
-            obs_circle.set_color(*(0.827, 0.827, 0.827, 0.65))
-            geoms.append(obs_circle)
        
         return geoms