diff --git a/README.md b/README.md
index 21807a1..7a91383 100644
--- a/README.md
+++ b/README.md
@@ -26,12 +26,14 @@ The rsband_local_planner combines an elastic band planner, a reeds shepp planner
 ![long_term_planning.gif](images/long_term_planning.gif)
 
 
-# Building the Docker Container
+# Building the Docker Container For This Repo
 
 ```
 $ ./build_docker.sh
 ```
 
+Start the container by running: 
+
 ```
 $ ./run_docker.sh
 ```
@@ -42,31 +44,23 @@ In the terminal launched by run_docker.sh, the first thing you will do is build
 $ source /opt/ros/melodic/setup.bash && catkin_make 
 ```
 
-and then launch the rsband_local_planner 
-
 
-Launch the [F1Tenth Simulator](https://github.com/pmusau17/Platooning-F1Tenth): 
+The next thing to do is Launch the [F1Tenth Simulator](https://github.com/pmusau17/Platooning-F1Tenth): 
 
-If you have the docker image built run the following 
+If you have the docker image built run the following:
 
 
 ```
 $ ./run_docker_simulator.sh
 ```
-In the docker terminal run: 
+Then run: 
 ```
-source devel/setup.bash && roslaunch race move_base_planning.launch
+$ source devel/setup.bash && roslaunch race move_base_planning.launch
 ```
 
 This should bring up a simulation of a racecar and some cones placed randomly throughout the racetrack. 
 
-In another terminal run:
-
-```
-$ ./run_docker.sh
-```
-
-In the docker terminal: 
+In the first docker terminal run:
 
 ```
 $ roslaunch rsband_local_planner move_base_planning.launch
@@ -76,4 +70,5 @@ This should launch the following rviz window:
 
 ![following_local_plan](images/following_local_plan.gif)
 
+
 In the above gif, the plans are generated using the rsband local planner proposed by [George Kouros](https://github.com/gkouros/rsband_local_planner) and tracked using a [pure pursuit controller](rsband_local_planner/scripts/pure_pursuit.py). The goals are sent by a node that is a rough implementation of a behaviorial layer. The idea here is that whenever the car gets within a 1 m radius of the goal point we say that the plan has been executed and send the next goal point. The behavioral node implementation node can be found [here](rsband_local_planner/scripts/behavioral_layer.py). The costmaps used throught the planning are updated using the vehicle's lidar sensor and make use of the [move_base](http://wiki.ros.org/move_base) ros package. The configuration files (these were a paaaaaaiiiiin) can be found [here](https://github.com/pmusau17/Planning-and-MPC/tree/main/rsband_local_planner/cfg).