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--- ease:workshop [2020/07/15 12:07] – s_fuyedc
+++ ease:workshop [2020/09/01 09:55] (current) – added video to intriduction hawkin
@@ Line 4: / Line 4: @@
 ==== Abstract ====
 In order to enable robots to perform every day activities with ease, they need to know when to do what. And who could be a better teacher then the human who wants the robots to perform these tasks? But instead of having us humans explain to the robots what to do, it is easier to just show them in a Virtual Reality environment, since the VR world can easily be adapted to whichever tasks we want to teach.
+In the following video we can see how a human performs everyday activities within the VR environment.
+<html>
+<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/199633754" style="position:absolute;top:0;left:0;width:100%;height:100%;" frameborder="0" allow="autoplay; fullscreen" allowfullscreen></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>
+<p><a href="https://vimeo.com/199633754">RobCoG</a> from <a href="https://vimeo.com/andreihaidu">Andrei Haidu</a> on <a href="https://vimeo.com">Vimeo</a>.</p>
+</html>
 Before we can teach the robots though, we have to understand what kind of data is being recorded and how we can access and inspect it, before we decide which parts of it to pass on to the robots.
 This tutorial will aim at teaching how to interact with episodic memories and inspect the data stored within using [[http://data.open-ease.org/user/sign-in|Open Ease]] and [[https://www.swi-prolog.org/|Prolog]] queries.
@@ Line 9: / Line 17: @@
 ==== Introduction ====
-We can record everything the human does in a Virtual Reality environment fairly precisely. The position of the head of the human can be tracked by tracking the headset itself, while the position of the hands can be mapped to the poistion of the joysticks. Every interaction between the hands of the human with the virtual environment is recorded. We can replay these recordings (episodes) and inspect them, learning from them how a human does certain every day activities. Why do we do certain tasks in a specific order? (E.g. in a table setting scenario, most people would put the plate down first and then get the cutlery.) How do we place objects? What orientation of objects do we tend to prefer?
+We can record everything the human does in a Virtual Reality environment fairly precisely. The position of the head of the human can be tracked by tracking the headset itself, while the position of the hands can be mapped to the position of the joysticks.
-All of these things are small subconscious decisions we are not necessarily aware of, since we are just used to do things a certain way. How should a robot know them? This is where episodic memories come in. We can use the recorded data from Virtual Reality to teach robots to do every day activities without having to hard code every small little detail into the program of the robot (at least that's the goal). Before we can get there though, we need to understand and see how the data is stored, what we can learn from it and what information can be obtained in the first place.
+Every interaction between the hands of the human with the virtual environment is recorded. We can replay these recordings (episodes) and inspect them, learning from them how a human does a certain everyday activity. Why do we do tasks in a specific order? (For instance, in a table setting scenario most people would put the plate down first and then get the cutlery.) How do we place objects? What orientation of objects do we tend to prefer?
+All of these things are small subconscious decisions we are not necessarily aware of since we are just used to do things a certain way. How should a robot know them? This is where episodic memories come in. We can use the recorded data from Virtual Reality to teach robots to do everyday activities without having to hard code every small little detail into the program of the robot (at least that's the goal). Before we can get there though, we need to understand and see how the data is stored, what we can learn from it, and what information can be obtained in the first place.
 ==== Tutorials ====
 == Setup the Tutorial Environment ==
-Go to the website [[http://data.open-ease.org]], preferably using Mozilla Firefox or Chrome (other browsers could work as well but have not been tested). You do not need to log in into the website to complete this tutorial. In the upper right corner, select ''Experiment Selection'' and from the appearing drop down menu select ''VR Breakfast (EASE) Milestone''.
+Go to the website [[http://data.open-ease.org]], preferably using Mozilla Firefox or Chrome (other browsers could work as well but have not been tested). You do not need to log in into the website to complete this tutorial. In the upper right corner, select ''Experiment Selection'' and from the appearing drop-down menu select ''VR Breakfast (EASE) Milestone''.
-Click on ''Teaching'' in the menu bar, ''Tutorials'' and in the tutorials overview select ''Fall School VR Tutorials''. Read and do this overview Tutorial which should look like this:
+Click on ''Teaching'' in the menu bar, ''Tutorials'', and in the tutorials overview select ''Fall School VR Tutorials''. Read and do this overview Tutorial which should look like this:
 {{ :ease:oe_introduction.jpg |}}
-The tutorial will explain a few basics, as well as describe the goals of the following tasks. If you want to get more familiar with OpenEase first and this is your first encounter with OpenEase, KnowRob and Prolog, it is advisable to also read and do the other tutorials available in the tutorial overview.
+The tutorial will explain a few basics, as well as describe the goals of the following tasks. If you want to get more familiar with OpenEase first and this is your first encounter with OpenEase, KnowRob, and Prolog, it is advisable to also read and do the other tutorials available in the tutorial overview.
 The following description comments on the build-in ''Virtual Reality NEEMs'' Tutorial within OpenEase, by trying to explain in more detail what certain queries do and why they are used the way they are. You can of course try to complete the Tutorial on your own without the information which is provided here. It's up to you.
-In order to be able to visualize what happened within an episode, we first need to load the environment in which that episode took place, as well as the episode in question. In short: we need to tell OpenEase what to load from the database and that we want the result to be visualized. In order to achieve this, select the ''Initialization'' query from the suggested queries in the bottom left corner. The entire query will then appear in the query editor, so that we can inspect which individual queries are called and edit them if needed be. In this case we can just keep the query as-is and submit it with the little button labled with a questionmark.
+In order to be able to visualize what happened within an episode, we first need to load the environment in which that episode took place, as well as the episode in question. In short: we need to tell OpenEase what to load from the database and that we want the result to be visualized. In order to achieve this, select the ''Initialization'' query from the suggested queries in the bottom left corner. The entire query will then appear in the query editor so that we can inspect which individual queries are called and edit them if needed be. In this case, we can just keep the query as-is and submit it with the little button labeled with a question mark.
 Now the environment, in this case a kitchen, will be loaded in the visualization overview panel. This might take some time to load, it can even take up several minutes, so don't worry if it is slow or some objects just appear as black cubes. This is normal.
@@ Line 42: / Line 55: @@
 Now these are a few queries chained together by a '','' which acts as an ''and'' in prolog. The fullstop ''.'' at the end is also very important. It signals the end of the query and you might get an error message if you forget to add it.
-''owl_parse'' parses the path of the EventData.owl file and the SemanticMap so what OpenEase knows which episode to load. The EventData contains every event that traspired in the episode. Every action like pick and place etc. is recorded as an Event in the EventData. The SemanticMap contains the initial state of the world, including which object is where, and how the world is set up. Aka if you do experiments in a kitchen, it will describe where all the kitchen Furniture and objects are, where the meshes are located etc. With these queries this information gets loaded into OpenEase.
+''owl_parse'' parses the path of the EventData.owl file and the SemanticMap so what OpenEase knows which episode to load. The EventData contains every event that happened in the episode. Every action like pick and place etc. is recorded as an Event in the EventData. The SemanticMap contains the initial state of the world, including which object is where, and how the world is set up. Aka if you do experiments in a kitchen, it will describe where all the kitchen Furniture and objects are, where the meshes are located etc. With these queries, this information gets loaded into OpenEase.
-''connect_to_db'' connects to the MongoDB database which contains all the poses of the objects during the Events. The poses are mapped  to timestamps.
+''connect_to_db'' connects to the MongoDB database which contains all the poses of the objects during the Events. The poses are mapped to timestamps.
 ''sem_map_inst'' asks for the current instance of a SemanticMap.
@@ Line 50: / Line 63: @@
 ''show'' visualizes the SemanticMap in the visualization pane.
-Note how after executing these queries you also get their results in the result pane above the question answering pane. In this case, we only had one varibale, namely ''MapInst'' (Everything starting with a capital letter is a variable for Prolog). This variable now contains the map instance of the semantic map that we have loaded and represents it like this:
+Note how after executing these queries you also get their results in the result pane above the question answering pane. In this case, we only had one variable, namely ''MapInst'' (Everything starting with a capital letter is a variable for Prolog). This variable now contains the map instance of the semantic map that we have loaded and represented it like this:
 <code prolog>
 MapInst = http://knowrob.org/kb/u_map.owl#SemanticEnvironmentMap_iW6S
 </code>
-Whenever we use variables, it can always be the case that there are multiple solutions to one question. If you want to know if this is the only solution or if there are more, click on the little arrow button right next to the questionmark button. There are solutions as long as you are able to click the button, and as long as you do not get a ''true'' or ''false'' statement in the result pane.
+Whenever we use variables, it can always be the case that there are multiple solutions to one question. If you want to know if this is the only solution or if there are more, click on the little arrow button right next to the question mark button. There are solutions as long as you are able to click the button, and as long as you do not get a ''true'' or ''false'' statement in the result pane.
-=== Tasks and Exercises ===
+==== Tasks and Exercises ====
 Now, let's see if you can figure out the following questions by yourself. There can be more then one way to ask the queries to obtain the result, so if you come up with a different solution then suggested here, that's totally fine.
-You can try to solve these without any hints at all. If you want some hints, under each task there will be some usefull queries mentioned that might help solving that particular task.
+You can try to solve these without any hints at all. In the following, first the task questions will be asked. Then there will be a secion with hints and usefull queries which might help to solve these tasks, and after that you will find the solutions.
+=== Task Questions ===
+Task 1: Which type of objects are brought by the demonstrators?
+Task 2: What are these objects' initial poses?
+Task 3: What are their final poses?
+Task 4: Highlight the human when he starts to grasp an object
+==== Usefull Predicates / Queries / Cheat Sheet ====
+Here are some usefull queries that might help you to solve the tasks. They are sectioned in two parts. The first contains general queries, the second ones are more NEEM and VR specific. This only means, that if you want to use them, you need to load the ''knowrob_robcog'' package first, which was the first step in the VR tutorial on the OpenEase website. Generally it is suggested to just try some predicates out to get a feeling for how they work and interact with each other, and build your solution query step by step.
+=== general Predicates ===
+<code prolog>
+entity(Action, [an, action, ['task_context', Context]]).
+</code>
+Definition: returns Action whose task context is Context
+variables: Context → Bound       Action → Unbound
+<code prolog>
+get_divided_subtasks_with_goal(Action,  Context,  SuccInstance, FailedInstances).
+</code>
+Definition: If an action, Action, has multiple subactions with the context Context in a way that it tries this subaction until it succeeds, this predicate returns successful instance and failed instances
+Variables: Action, Context → Bound; SuccInstance, FailedInstances → Unbound
+<code prolog>
+task_start(Act, St) & task_end(Act, End).
+</code>
+Definition: returns start and end time point of given action, Act
+Variables: Act → Bound; Start, End → Unbound
+<code prolog>
+entity(Base, [an, object, [srdl2comp:urdf_name, 'base_link']]).
+</code>
+Definition: returns base link individual
+Variables: Base → Unbound
+<code prolog>
+object_pose_at_time(Obj, TimePoint, mat(Pose)).
+</code>
+Definition: returns Obj’s pose at TimePoint. For non-moving objects, you can use 1 as TimePoint.
+Variables: Obj→ Bound; Timepoint → Bound; Pose → Unbound
+<code prolog>
+owl_individual_of(Obj, Type).
+</code>
+Definition: returns objects with the given type or returns the types of the object. So, both Obj and Type can be bound and unbound
+Hint: Type that is important to consider: knowrob:’HingedJoint’
+<code prolog>
+transform_between(Pose1, Pose2, [_, _, RelPosition, RelRot]).
+</code>
+Definition: returns Pose1’s position and rotation with respect to Pose2.
+Variables: Pose1, Pose2 → Bound   RelPosition, RelRot → Unbound
+<code prolog>
+findall(Var, (PrologQuery), ListOfVar).
+</code>
+Definition: finds out all of the solutions of PrologQuery. Then, it stores all of solutions of the unbound Var inside the List ListOfVar.
+<code prolog>
+jpl_list_to_array(List,  Array).
+</code>
+Definition: converts Prolog List to Java Array.
+<code prolog>
+append(List1, List2, NewList).
+</code>
+Definition: appends two lists together
+<code prolog>
+generate_feature_files(FeatureArrArr, Path)
+</code>
+Definition: given an float array of array, write this as a feature file (with CSV extension) into the given path
+=== VR/NEEM specific Predicates ===
+These are some queries which are within the ''knowrob_robcog'' package or rather NEEM/VR specific. So they might not always be defined if used with other data but for this tutorial they can be very useful. You can replace the variables with whatever you like, but these names provide hints as towards what they can be used for in the tasks.
+<code prolog>
+ep_inst(EpInst).
+</code>
+Definition: returns the instance of an Episode.
+<code prolog>
+u_occurs(EpInst, EventInst, Start, End).
+</code>
+Definition: Given the Episode Instance, returns any occured Event Instance with the correspoding Start and End time stamps of the Event.
+<code prolog>
+obj_type(EventInst, knowrob:'GraspingSomething').
+</code>
+Definition: Returns an Event Instance in which an Event of Type ''GraspingSomething'' occured. Can be also used to return an object of a specific type.
+<code prolog>
+rdf_has(EventInst, knowrob:'objectActedOn', ObjInst).
+</code>
+Definition: Checks if rdf has an Object Instance within the given Event Instance with the propery ''objectActedOn''.
+<code prolog>
+iri_xml_namespace(ObjInst, _, ObjShortName).
+</code>
+Definition: Cuts of the Namespace-prefix of the Object Instance, returning it's short Name.
+<code prolog>
+actor_pose(EpInst, ObjShortName, Start, PoseObjStart).
+</code>
+Definition: returns the Pose of the Object (ObjShortName) during a specific Timestamp (Start) from the given Episode Instance. The Pose will be an array of seven values. The first three are the x y z coordinates, while the last 4 are x y z w quaternion values.
+Note: You need to use the ''iri_xml_namespace'' query to shorten the name. Otherwise this query might not work properly.
+<code prolog>
+show_world_state(EpInst, Start).
+</code>
+Definition: visualizes the world state of a specific Episode Instance during a given Time Stamp.
+<code prolog>
+highlight(Object).
+</code>
+Definition: Highlights a specific Object Instance in red within the visualization pane.
+==== Solution suggestions ====
+The following contains some solution suggestions. In the first task, we will show multiple solution methods, to showcase how different some approaches can be. After this only one solution will be presented. If you get the same result using different predicates, or even some which are not mentioned above, that's totally fine.
+The Idea behind each solution will be briefly explained before the Solution itself is shown. It can also be used as a hint towards finding a solution on your own.
 == Task 1: Which type of objects are brought by the demonstrators? ==
+**Solution 1**
+Idea: Get an episode instance. Check if the episode has an event instance with a start and end time. Return an event instance which is of type ''GraspingSomething''. Get the object instance of that event, which fulfills the property ''objectActedOn''. Return the Type of the Object. Use Findall to find all the types of objects which have been manuipulated by the demonstrator, return them in a list.
-**Solution:**
+''Note:'' Some of the solutions will contain screenshots which are supposed to show the visualization of the result and the state of the world. Unfortunately, at the time of the writing of this tutorial, there was a bug which highlights all the objects instead of just one, so that everything appears red. The screenshots will be updated once this is fixed. Still, the position changes of the interacting person and the objects can be seen.
+<code prolog>
+findall(ObjType,
+    (ep_inst(EpInst),
+     u_occurs(EpInst, EventInst, Start, End),
+     obj_type(EventInst, knowrob:'GraspingSomething'),
+     rdf_has(EventInst, knowrob:'objectActedOn', ObjInst),
+     obj_type(ObjInst, ObjType)),
+     ListOfObjectTypes).
+</code>
+Result of the Query:
+<code prolog>
+[...]
+ListOfObjectTypes = [
+= knowrob:KoellnMuesliCranberry
+= knowrob:KoellnMuesliCranberry
+= knowrob:BaerenMarkeFrischeAlpenmilch18
+= knowrob:BaerenMarkeFrischeAlpenmilch18
+= knowrob:BowlLarge
+= knowrob:BowlLarge
+= knowrob:SpoonSoup
+= knowrob:SpoonSoup
+]
+</code>
+**Solution 2**
 <code prolog>
 findall(_T,
@@ Line 80: / Line 248: @@
 = knowrob:CerealFood
 = knowrob:Granular
-...
+[...]
-<code>
+</code>
 == Task 2: What are these objects' initial poses? ==
+Idea: Very similar to task 1. Just without the findall since we only need one instance here. So going from the solution of task 1, the name of the object was shortened. The position of the object at the start of the ''GraspingSomething'' event was determined. The world state was updated to show the world at the time of the start of the event. The object should be highlighted.
 **Solution**
+<code prolog>
+ep_inst(EpInst),
+u_occurs(EpInst, EventInst, Start, End),
+obj_type(EventInst, knowrob:'GraspingSomething'),
+rdf_has(EventInst, knowrob:'objectActedOn', ObjInst),
+obj_type(ObjInst, ObjType),
+iri_xml_namespace(ObjInst, _, ObjShortName),
+actor_pose(EpInst, ObjShortName, Start, PoseObjStart),
+show_world_state(EpInst, Start),
+highlight(ObjInst).
+</code>
+**Result:**
+<code prolog>
+ObjShortName = KoellnMuesliCranberry_2pcO
+EpInst = http://knowrob.org/kb/unreal_log.owl#UnrealExperiment_Hnkn
+PoseObjStart = [
+= -3.5357048511505127
+= -2.2400009632110596
+= 1.0195969343185425
+= 0.8191515803337097
+= 0.000012126906767662149
+= -0.00002010483331105206
+= 0.5735770463943481
+]
+EventInst = http://knowrob.org/kb/unreal_log.owl#GraspingSomething_1uAu
+Start = http://knowrob.org/kb/unreal_log.owl#timepoint_6.434098
+ObjType = knowrob:KoellnMuesliCranberry
+End = http://knowrob.org/kb/unreal_log.owl#timepoint_8.321757
+ObjInst = http://knowrob.org/kb/unreal_log.owl#KoellnMuesliCranberry_2pcO
+</code>
+{{ :ease:solution_2.png |}}
 == Task 3: What are their final poses? ==
+Idea: Same as the above except for the timestamp change from start to end.
+**Solution**
+<code prolog>
+ep_inst(EpInst),
+u_occurs(EpInst, EventInst, Start, End),
+obj_type(EventInst, knowrob:'GraspingSomething'),
+rdf_has(EventInst, knowrob:'objectActedOn', ObjInst),
+obj_type(ObjInst, ObjType),
+iri_xml_namespace(ObjInst, _, ObjShortName),
+actor_pose(EpInst, ObjShortName, End, PoseObjStart),
+show_world_state(EpInst, End),
+highlight(ObjInst).
+</code>
+**Result:**
+<code prolog>
+ObjShortName = KoellnMuesliCranberry_2pcO
+EpInst = http://knowrob.org/kb/unreal_log.owl#UnrealExperiment_Hnkn
+PoseObjStart = [
+= -3.984745979309082
+= -1.8089243173599243
+= 0.9824687242507935
+= 0.9975854754447937
+= 0.006023405119776726
+= -0.023459946736693382
+= 0.06508690118789673
+]
+EventInst = http://knowrob.org/kb/unreal_log.owl#GraspingSomething_1uAu
+Start = http://knowrob.org/kb/unreal_log.owl#timepoint_6.434098
+ObjType = knowrob:KoellnMuesliCranberry
+End = http://knowrob.org/kb/unreal_log.owl#timepoint_8.321757
+ObjInst = http://knowrob.org/kb/unreal_log.owl#KoellnMuesliCranberry_2pcO
+</code>
+{{ :ease:solution_3.png |}}
 == Task 4: Highlight the human when he starts to grasp an object ==
+Idea: Same as the above. The new change is that we want the position of the person. The person which interacts with the obejcts is refered to as ''Camera'' though, since this is where the camera would be. This is important for perception.
+**Solution**
+<code prolog>
+ep_inst(EpInst),
+u_occurs(EpInst, EventInst, Start, End),
+obj_type(EventInst, knowrob:'GraspingSomething'),
+rdf_has(EventInst, knowrob:'objectActedOn', ObjInst),
+obj_type(ObjInst, ObjType),
+iri_xml_namespace(ObjInst, _, ObjShortName),
+rdf_has(CameraInst, rdf:type, knowrob:'CharacterCamera'),
+iri_xml_namespace(CameraInst, _, CameraShortName),
+show_world_state(EpInst, Start),
+actor_pose(EpInst, CameraShortName, Start, CameraStartPose),
+highlight(CameraInst).
+</code>
+**Result:**
+<code prolog>
+EpInst = http://knowrob.org/kb/unreal_log.owl#UnrealExperiment_Hnkn
+CameraStartPose = [
+= -3.3242106437683105
+= -1.7523821592330933
+= 1.5412843227386475
+= -0.4662284851074219
+= -0.24837808310985565
+= -0.10210808366537094
+= 0.8429195284843445
+]
+End = http://knowrob.org/kb/unreal_log.owl#timepoint_8.321757
+CameraShortName = CharacterCamera_LPbi
+ObjInst = http://knowrob.org/kb/unreal_log.owl#KoellnMuesliCranberry_2pcO
+ObjShortName = KoellnMuesliCranberry_2pcO
+EventInst = http://knowrob.org/kb/unreal_log.owl#GraspingSomething_1uAu
+ObjType = knowrob:KoellnMuesliCranberry
+Start = http://knowrob.org/kb/unreal_log.owl#timepoint_6.434098
+CameraInst = http://knowrob.org/kb/unreal_log.owl#CharacterCamera_LPbi
+</code>
+{{ :ease:solution_4.png |}}
-=== Conclusion ===
+==== Best practices and Feedback ====
+=== Best practices ===
+  * queries one by one, stacking them on one another. Once you know how some of them work, you can build on top of that.
+  * Ask questions if you have the chance to.
+  * Vizualization can be very helpful (if it works).
+=== Feedback ===
+If you have any suggestions or feedback about this tutorial, or if you encounter bugs, feel free to contact us. We appreciate any given feedback :)
-=== general todos ===
-@TODO link Prolog, KnowRob, OpenEase whenever they are mentioned
-@TODO make sure the used terms for the individual OpenEase panels are correct