Merge pull request cram2#202 from rhaschke/fix-docs

Fix issues in documentation

examples/interface_examples/giskard.md

@@ -15,7 +15,7 @@ jupyter:
 # Giskard interface in PyCRAM
 
 This notebook should provide you with an example on how to use the Giskard interface. This includes how to use the
-interface, how it actually works and how to extend it.
+interface, how it actually works, and how to extend it.
 
 We start by installing and launching Giskard. For the installation please follow the
 instructions [here](https://github.com/SemRoCo/giskardpy).

examples/intro.md

@@ -30,10 +30,10 @@ A BulletWorld can be created by simply creating an object of the BulletWorld cla
 ```python
 from pycram.worlds.bullet_world import BulletWorld
 from pycram.world_concepts.world_object import Object
-from pycram.datastructures.enums import ObjectType
+from pycram.datastructures.enums import ObjectType, WorldMode
 from pycram.datastructures.pose import Pose
 
-world = BulletWorld()
+world = BulletWorld(mode=WorldMode.GUI)
 ```
 
 The BulletWorld allows to render images from arbitrary positions. In the following example we render images with the
@@ -342,6 +342,8 @@ cereal = Object("cereal", ObjectType.BREAKFAST_CEREAL, "breakfast_cereal.stl", p
 ```
 
 ```python
+from pycram.datastructures.enums import Grasp
+
 cereal_desig = ObjectDesignatorDescription(names=["cereal"])
 kitchen_desig = ObjectDesignatorDescription(names=["kitchen"])
 robot_desig = ObjectDesignatorDescription(names=["pr2"]).resolve()
@@ -355,7 +357,7 @@ with simulated_robot:
 
     NavigateAction(target_locations=[pickup_pose.pose]).resolve().perform()
 
-    PickUpAction(object_designator_description=cereal_desig, arms=[pickup_arm], grasps=["front"]).resolve().perform()
+    PickUpAction(object_designator_description=cereal_desig, arms=[pickup_arm], grasps=[Grasp.FRONT]).resolve().perform()
 
     ParkArmsAction([Arms.BOTH]).resolve().perform()
 
@@ -380,10 +382,10 @@ Task trees are a hierarchical representation of all Actions involved in a plan.
 inspect and restructure the execution order of Actions in the plan.
 
 ```python
-import pycram.task
+import pycram.tasktree
 import anytree
 
-tt = pycram.task.task_tree
+tt = pycram.tasktree.task_tree
 print(anytree.RenderTree(tt))
 ```
 

examples/language.md

@@ -180,7 +180,7 @@ with simulated_robot:
 ## Combination of Expressions
 
 You can also combine different language expressions to further structure your plans. If you combine sequential and
-parallel expression please keep in mind that sequential expressions bind stringer than parallel ones. For example:
+parallel expression please keep in mind that sequential expressions bind stronger than parallel ones. For example:
 
 ```
 navigate | park + move_torso

examples/location_designator.md

@@ -68,9 +68,9 @@ print(pose)
 
 ## Reachable
 
-Next we want to locations from where the robot can reach a specific point, like an object the robot should pick up. This
+Next we want to have locations from where the robot can reach a specific point, like an object the robot should pick up. This
 can also be done with the {meth}`~pycram.designators.location_designator.CostmapLocation` description, but this time we need to provide an additional argument.
-The additional argument is the robo which should be able to reach the pose.
+The additional argument is the robot which should be able to reach the pose.
 
 Since a robot is needed we will use the PR2 and use a milk as a target point for the robot to reach. The torso of the
 PR2 will be set to 0.2 since otherwise the arms of the robot will be too low to reach on the countertop.
@@ -97,7 +97,7 @@ print(location_description.resolve())
 As you can see we get a pose near the countertop where the robot can be placed without colliding with it. Furthermore,
 we get a list of arms with which the robot can reach the given object.
 
-## Visibile
+## Visible
 
 The {meth}`~pycram.designators.location_designator.CostmapLocation` can also find position from which the robot can see a given object or location. This is very
 similar to how reachable locations are described, meaning we provide a object designator or a pose and a robot
@@ -215,7 +215,7 @@ print(access_location.pose)
 
 ## Giskard Location
 
-Some robots like the HSR or the Stretch2 need a full-body ik solver to utilize the whole body. For this case robots
+Some robots like the HSR or the Stretch2 need a full-body ik solver to utilize the whole body. For this case
 the {meth}`~pycram.designators.specialized_designators.location.giskard_location.GiskardLocation` can be used. This location designator uses giskard as an ik solver to find a pose for the
 robot to reach a target pose.
 

examples/migrate_neems.md

@@ -23,7 +23,7 @@ connect your pycram process to it.
 
 After you recorded your data locally you can migrate the data using the `migrate_neems` function.
 
-First, lets create an in memory database engine called `source_engine` where we record our current process.
+First, lets create an in-memory database engine called `source_engine` where we record our current process.
 
 ```python
 import sqlalchemy.orm

examples/motion_designator.md

@@ -16,7 +16,7 @@ jupyter:
 
 Motion designators are similar to action designators, but unlike action designators, motion designators represent atomic
 low-level motions. Motion designators only take the parameter that they should execute and not a list of possible
-parameters, like the other designators. Like action designators, motion designators can be performed, performing motion
+parameters, like the other designators. Like action designators, motion designators can be performed. Performing a motion
 designator verifies the parameter and passes the designator to the respective process module.
 
 Since motion designators perform a motion on the robot, we need a robot which we can use. Therefore, we will create a

examples/orm_example.md

@@ -15,7 +15,7 @@ jupyter:
 # Hands on Object Relational Mapping in PyCram
 
 This tutorial will walk you through the serialization of a minimal plan in pycram.
-First we will import sqlalchemy, create an in memory database and connect a session to it.
+First we will import sqlalchemy, create an in-memory database and connect a session to it.
 
 ```python
 import sqlalchemy.orm