- ((.|\n)*?)
RLBench Test Page
+ """ + html_bottom = """ + + + """ + + new_html = html_top + new_entry + '\n'.join(uls) + html_bottom + with open('index.html', 'w') as f: + f.write(new_html) + + # Will be 0 if tests ran OK + sys.exit(fail) diff --git a/external/rlbench/travisci_run_tests.py b/external/rlbench/travisci_run_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..494a447c91ed94ed0c290c9e820d4910fa08cba2 --- /dev/null +++ b/external/rlbench/travisci_run_tests.py @@ -0,0 +1,34 @@ +from unittest import TestSuite, defaultTestLoader +from HtmlTestRunner import HTMLTestRunner +import sys +import os + +TEST_TYPES = ['unit', 'demos'] + + +if __name__ == '__main__': + + test_folder = 'tests' + + assert len(sys.argv) == 4 + + test_type = sys.argv[1] + branch = sys.argv[2] + build_num = sys.argv[3] + + assert test_type in TEST_TYPES + + test_suite = TestSuite() + all_test_cases = defaultTestLoader.discover( + os.path.join(test_folder, test_type), 'test*.py') + # Loop the found test cases and add them into test suite. + for test_case in all_test_cases: + test_suite.addTests(test_case) + runner = HTMLTestRunner( + output='reports/%s' % str(build_num), + report_name=test_type, + add_timestamp=False, combine_reports=True) + result = runner.run(test_suite) + + # Will be 0 if tests ran OK + sys.exit(1 if len(result.failures) + len(result.errors) > 0 else 0) diff --git a/external/rlbench/tutorials/complex_task.md b/external/rlbench/tutorials/complex_task.md new file mode 100644 index 0000000000000000000000000000000000000000..857784d438b63dabf59636058823324937f4a988 --- /dev/null +++ b/external/rlbench/tutorials/complex_task.md @@ -0,0 +1,310 @@ +# Complex Task Walkthrough + +Here we will walk through the entire process of building a more complex task, empty_container. Hopefully you will have something that looks like the following by the end: + +
+
+This tutorial will assume some of the knowledge learnt in the Simple Task Walkthrough, so make sure to work through that first. As for every task built, we will again go through each of the stages: (1) creating the task through the task builder, then (2) creating the task by adding objects to the scene in a V-Rep simulator instance, (3) coding the Python File, then finally (4) passing the Task Validator.
+
+In addition to the features common to all tasks, this task will need to excute the following:
+* **Dynamically Sample and Spawn Procedural Objects** - These are a collection of various small shapes as models downloaded as .obj and .mtl files from https://sites.google.com/site/brainrobotdata/home/models. If you wish to understand how these object files fit into the repository, examine the rlbench/assets/procedural_objects directory which at minimum must contain the {*object_name*} and {*object_name*}*\_coll* files for every object.
+* **Setting target container colors** - The target containers will cycle through the colors list found in *rlbench/backend/const.py* with every change in variation.
+* **Grasping** - Many tasks require the Panda to grasp and pick objects up. We will see how waypoints can be given extension strings to control this functionality.
+* **Dynamically Repeat Waypoints** - For every object to be removed from the large container, the same set of waypoints will be used, with the Panda's path being re-calculated for every remaining object to be moved. The only difference between these paths will be the position of the grasping dummy *waypoint1*, so we will need to define a function which re-positions this dummy to another object with every repetition.
+* **Account for Failed Grasps** - Many of the procedural objects are awkwardly shaped and thus difficult to grasp. When the Panda fails to grasp an item at *waypoint1*, it will still complete its path (if reachable) with nothing grasped. In order to raise the percentage of succesful demos and thus pass the task validator, we would like to account for this. This will be done by assessing which objects have been succesfully moved at every time step in the *step()* function, and instructing the task to re-position and repeat waypoints until all items have been moved to the target container.
+* **Cleanup Dynamic Objects** - The state of the scene before a demo must match the state of the scene upon demo completion. As such, we will use the *cleanup()* function to remove all of the procedural objects from the V-Rep scene upon completion of every demo.
+
+## A note on Grasping and Physics in V-Rep
+It is worth discussing here how the Panda grasps objects in the simulator, and how this differs from how a human hand would grasp in the real world. Rather than rely on friction and reactionary force as a person would when picking something up, RLBench and PyRep simplify physics calculations (and thus speed up and smooth out simulation) by making any object upon which the Panda's gripper closes on a child of the Panda tip, as long as said object has been registered as graspable in the python file. This keeps the object's pose stationary relative to the Panda tip while it is grasped, and the object is released from this relationship when it is returned to its original place in the Scene Hierarchy tree upon the re-opening of the gripper. This can even be noticed in the animation of empty_container above; see the objects labeled 'Shape' followed by a number dissapear from view in the scene hierarchy when grasped (in actual fact they are still there, but are moved to into a node of the Panda's collapsed sub-tree) and re-appear when dropped.
+
+Note then that what matters for grasping, and for the entire task, is not that every physical property of every object matches exactly what it would be in real life, but that succesfully completed demos behave in such a way that a model could theoretically learn how some of these common items would behave in common tasks. In practice it is okay for a small item that might have a mass of 0.1kg in real life to have its mass set to 0.5 or 5kg in the simulation, as long as the task environment is reflective of the actions and interactions that would be happening in real life. Some objects, like the hanger in the *move_hanger* task, will have a certain 'visual' object as a child of a similar, more simple 'physical' object that is set to invisible (by inverting its camera visibility layers - more on this later) in order to speed up the task. This is done because having every object in a simulation be either a simple, convex shape or a grouping of such massively simplifies the calculations performed by the physics engine, helping to eradicate buggy and unrealistic behaviours which you may otherwise encounter.
+
+Workarounds are sometimes useful, and do not necessarily impact the quality of the task and its contribution to the data set. For example, when creating the peppers in weighing_scales, it was found that they would naturally slide towards one direction when left alone on a flat surface, as if that surface was somehow tilted. Adding an 'invisible' block to the base of the peppers increased their surface area and stopped this from happening without affecting the quality of either (a) the visual information available to the panda throughout the task or (b) the actions the Panda would take to succesfully complete it. You will likely encounter unrealistic and frustrating behaviors of objects in the physics simulator at some point - look at existing tasks to inspire creative workarounds that maintain the high quality of this dataset.
+
+## 1 Creating the Scene in V-Rep
+## Creating the Large Container
+First we will create the large container by creating 5 seperate cuboids, one for each plane of the container, then grouping these cuboids such that they can be treated as a single shape in both the V-Rep instance and the Python file. Create a cuboid with by clicking Add -> Primitive shape -> Cuboid and setting its dimensions as follows:
+
+
+
+Hit enter/okay and rename this cuboid *large_container0*, make it a child of the *empty_container* dummy in the scene hierarchy, then drag it up to or place it on the table's surface. Now we will change its color to grey by double clicking on its name in the scene hierarchy to open the Scene Object Properties; click on *Adjust color* to open the Shape dialog, then select *Ambient/diffuse component* and adjust the parameters as seen below. Note that as RGB and HSL are simply two parametizations of an item's color, only one of them needs to be set. The other one will adjust itself automatically.
+
+
+
+Next we will create the sides of the container. Add another cuboid with dimensions {*3.00e-01*, *6.00e-03*, *1.5e-03*} and open its Rotation/Orientation dialog by selecting the cuboid in the Scene Hierarchy, clicking on the Rotation icon in the upper left hand side of the window, and setting its orientation (relative to the world) as below.
+
+
+
+Once you have done that, rename it to *large_container1* and make it a child of *large_container0* in the Scene Hierarchy, then move up to the table top it by opening the mouse translation dialog, selecting mouse translation, ensuring that only the *along z* box is ticked, and dragging it it by holding the left mouse click. Remember that more precise mouse translation can be achieved by holding shift whilst dragging the object once the left mouse button has been held down. Then place it such that its edge is adjacent to the edge of the container base by performing a mouse translation whilst only *along Y* is selected. You should now have a Scene and Scene Hierarchy which looks like the following:
+
+
+
+Now we need to create and postion the rest of the large container's walls. Copy and paste the wall by selecting *large_container1* in the Scene Hierarchy and doing Ctrl+C then Ctrl+V, this will create *large_container2* which you must then make a child of the task dummy. Now we will rotate the object by performing a mouse rotation: ensuring *large_container2* is selected, open the rotation dialog, and select *Relative to: World* and *about Z*. Holding down the left mouse click, drag the mouse around until the wall has rotated 90 degrees anti-clockwise like so:
+
+
+
+We will now use a trick to place the second wall, which will work so long as *large_container1* has been perfectly centered with *large_container0* along the Y Axis (ensure this is the case by checking that the position of *large_container1* relative to its parent frame is 0.00 in the X-coord box). We can simply take make *large_container2*'s X-coord relative to its parent frame the negative of *large_container1*'s relative Y-coord position, and then set *large_container2*'s relative Y-coord position to that of *large_container1*'s relative X-coord position like below. Your *large_container1* might not have the exact same relative X and Z co-ordinates and that's perfectly okay, what matters here is that the described relationships hold:
+
+
+
+Repeat this process to create and postion the remaining two walls, working out how to position the walls for yourself. This should leave you with the following:
+
+
+
+Now we will group each of the large_container objects together in order to end up with a single object which we can manipluate and reference. While holding down the Shift key, select each of the large container objects, ensuring that you click on *large_container0* last (this is done because the created group will be names after the object last clicked on), then right click on the grouping in the Scene Hierarchy. Select Edit->Grouping / Merging->Group selected shapes like so:
+
+
+
+Rename the resulting grouped object to *large_container*. Set all 4 of its *Object special properties*, and ensure that it is set to to *Respondabl* but not *Dynamic* by clicking on *Show dynamic properties* under the *Common* tab of the Scene Object Properties dialog.
+
+#### A note on Grouping and Merging
+Both grouping and merging allow us to treat a collection of objects as a single object, but there are some key distinctions between the two actions which will affect how we choose which action to use. As previously mentioned, the physics engine runs far faster and smoother when all shapes for which it makes calculations are convex. If a *merge* would result in a non-convex object being created, potential problems arise. Merging does, however, have the advantage of creating a tighter coupling between objects for the purpose of dynamic motion, ie motion that occurs as a result of physical laws like gravity as opposed to motion that is the direct result of scene manipulation, such as if the Panda were to grasp and move an object. This means that any grouping of objects will not preserve the relative poses of the grouped objects. For example, if we had instead created a mug object by grouping a mug body and handle and then attempted to pick up the mug by its handle, it would come detached from the body, as if they were never actually connected. In this case a merging would result in a non-convex shape, so we could instead make the mug body a child of the handle and hope that the body never has a force applied onto it that is not also applied to the handle (the mug body remains stationary relative to the handle when the handle is moved because of parent-child relationship but the reverse relationship would not hold!), or better yet find a way to create the mug as a single convex object (more on this in another document).
+
+That said, these problems only apply if such objects are made *dynamic*, only then can external forces other than the actions of the Panda and forces resulting from an object collision act on our objects in the V-Rep simulator. All containers to be added in this task will have their dynamic properties turned off, meaning that their pose will stay static unless an object in the scene were to do work on it, which it will not. You will however, need to ensure they are set to *Respondable*. As such, either grouping or merging would work just fine here and only actually serve the purpose of allowing us to treat the container as a single object in both the V-Rep scene and the Python File.
+
+#### Creating the Target Containers
+Now that you have learnt how to create and compose container object from simple shapes, it is left up to you to create the two smaller target containers seen in this walthrough's first gif animation, which should be named *small_container0* and *small_container1*. The only differences between the large and target containers are (1) the targets' walls should have their *Alpha* orientation value set to 90 rather than 55 degrees once instantiated, (2) they need to be postioned slightly to either siide of the large container, and (3) they will be slightly smaller. Their absolute size does not matter too much, but they will need to be large enough to hold the collection of procedural objects spawned in the task. Finally, it is recommended that you first create one target container, position it to the side of the target container, then and copy and paste it, making sure to position it exactly on the opposite side of the large_container. The color of these target_containers will be set dynamically in the python file so you do not need to worry about setting it here. Set their *Object special* and *Dynamic* properties. Once you have completed this step, your scene should appear as below:
+
+
+
+#### Adding Proximity Sensors
+We now need to add 2 proximity sensors, and they need to be positioned such that they can detect all objects placed inside the target containers. Add a *Pyramid type* proximity sensor and change its volume parameters (by clicking on *show volume parameters in its Scene Object Properties dialog* such that all X and Y fields are equivalent to the X Y dimensions of your target container's base, and its *Range* set to the height of your target container's walls. Remember to set the *Offset* parameter to 0 like so, and remember also that your volume parameters may differ slightly from the ones shown here because your target container volume will likely not be the exact same as the ones created here:
+
+
+
+Once you have added this, name it *success0*, make it a child of *small_container0*, and set its position relative to its parent to 0 in all fields. Copy and paste this sensor to create *success1*, and again make it a child of *small_container1*, setting its relative position to 0.
+
+#### Adding Waypoints
+The last step we need to do is create waypoint dummies. This task requires 4 waypoints:
+
+* **waypoint0**: the approach waypoint, positioned at the center of *large_container*'s base and roughly 20 cm above it.
+* **waypoint1**: the grasping waypoint. This waypoint's position will be set dynamically in the python file every time a new object needs to grasped so its position does not matter too much; here it has been placed halfway between *large_container*'s base and *waypoint0*.
+* **waypoint2**: the lifting waypoint. The purpose of this waypoint is to raise objects up to a reasonable height such that the panda can then move objects horizontally without colliding into any of the objects we have created. We will positon this waypoint in the exact same location as *waypoint0*.
+* **waypoint3**: the drop waypoint. The panda will release items from its grasp when this waypoint is reached and we will place this waypoint above the center of either *small_container0*. Depending on the variation, the python file will sometimes place this waypoint above *small_container1*. This dummy will have the same height as waypoints 0 and 2.
+
+Begin by adding a dummy and naming it waypoint 0. Set its position as described above (make the dummy a child of *large_container* and set its relative positional vector to {0, 0, 0.17}), and adjust its orientation (relative to the world) as to {180, 0, 180}, naming it *waypoint0*. One thing to be aware of is that sometimes V-Rep will struggle to set a position or orientation to absolute 0, instead setting an arbritratily small value as below:
+
+
+
+Next, copy and paste this waypoint, lowering its position by any means you wish to use to roughly halfway between *waypoint0* and the table's surface. After this copy and paste *waypoint0* again to create *waypoint3*, and leave it as is. Finally, do one final copy and paste to create *waypoint4*, and in the same way that we set the position of *waypoint0*, position it centrally over *small_container0*. Then if not already so, ensure that these waypoints are all children of large_container. Once you have done this, your scene and scene hierarchy should look like the following:
+
+
+
+#### Extension Strings
+We must assign extension strings to some of the waypoints in order to control grasping and ungrasping. In order to do this, we will need to open the Scene Object Properties dialog for dummies for which we wish to add such strings, select the *Common* tab at the top of the dialog, and typing in the strings ourselves in the *Extension String* text box found under *General properties*. We want to make sure that the gripper is open and ready to grasp once it reaches *waypoint0*, as such set its extension string like so:
+
+
+
+We will set *waypoint1*'s extension string to *close_gripper()* in the same manner described above. Here we will also need to account for two seemingly contradictory factors: (1) some of the procedural objects will be spawned in such a manner where they occlude or are in contact with one another and as such grasping one would require colliding with the other and (2) path planning as we have used it so far only calculates paths if collisions can be avoided. Luckily, an extension string *ignore_collisions* exists which instructs the path planning algorithm to do just that. We can assign multiple extension strings to a single waypoint separated by a comma or semi-colon like so (do this for your *waypoint1*):
+
+
+
+Note here that the waypoint succeeding any waypoint with the *close_gripper* extension must have *ignore_collisions* set. As such we will add this to *waypoint2*, and add *open_gripper()* to *waypoint3* in order to instruct the panda to drop items into the small container.
+
+#### Adding the Spawn Boundary
+The final step in completing our task model is to add a spawn boundary so that we can instruct the dynamic spawning of procedural objects in the python file like the one below:
+
+
+
+The simplest way to do this is probably by copying and pasting the workspace and aligning its' center with that of the *large_container* by setting it as a child of *large_container* then setting its relative pos to 0. Perform a mouse translation along the z axis such that the plane is vertically positioned roughly halfway between the base and the upper most edge of the container, then renaming it to *spawn_boundary*. Next we will adjust the size of the plane so that it is just a little smaller than the size of *large_container*'s base. Open *spawn_boundary*'s geometry dialog by clicking on *View/Modify geometry* in the *Scene Object Properties* dialog under the *Shape* tab, unchecking *Keep proportions*, and adjusting the X and Y fields under *Bounding box size*. Keeping the Z value constant, use trial and error to set values which make *spawn_boundary* a square plane just smaller than the base of *large_container*. Finally, make *spawn_boundary* a child of large container as shown in the image above.
+
+To finish this step, we will invert *spawn_boundary*'s visiblity layers so that it will not be visually present in the task. In general, this is done by opening the *Scene Object Properties* dialog, opening the *Common* tab, and under *Visibility* unchecking all the boxes in the first row and checking the equivalent boxes in the row just beneath it:
+
+
+
+Make sure to save the .ttm by inputting 's' in the task_validator when simulation is not running:
+
+
+
+We are now ready to begin coding the Python file.
+
+## 2 Writing the Python File
+First we need to ensure that we import the necessary classes like below. In practice, this is normally done as you write the script, but we can do it all in one go for the purposes of this walkthrough:
+
+```python
+from typing import List, Tuple
+from rlbench.backend.task import Task
+from rlbench.const import colors
+from rlbench.backend.task_utils import sample_procedural_objects
+from rlbench.backend.conditions import ConditionSet, DetectedCondition
+from rlbench.backend.spawn_boundary import SpawnBoundary
+import numpy as np
+from pyrep.objects.shape import Shape
+from pyrep.objects.proximity_sensor import ProximitySensor
+from pyrep.objects.dummy import Dummy
+```
+
+### init_task() -> None:
+Here we will first instantiate object handles, remembering that the handle to a spawn boundary shape must later be passed as a list when instantiating a *SpawnBoundary* object.
+
+```python
+self.large_container = Shape('large_container')
+self.target_container0 = Shape('small_container0')
+self.target_container1 = Shape('small_container1')
+self.spawn_boundaries = [Shape('spawn_boundary')]
+self.success_detector0 = ProximitySensor('success0')
+self.success_detector1 = ProximitySensor('success1')
+```
+We will then call two functions, *register_waypoint_ability_start()* and *register_waypoints_should_repeat()* like so:
+
+```python
+self.register_waypoint_ability_start(1, self._move_above_object)
+self.register_waypoints_should_repeat(self._repeat)
+```
+
+The first of these simply allows us to have the task execute some kind of code or functionality before moving towards this waypoint, in this case we will be defining *_move_above_next_object* which will move *waypoint1* (the grasping waypoint) to the center of the next procedural object which we will attempt to pick up. *register_waypoints_should_repeat* allows us to define for ourselves the conditions on which the panda will continue to cycle through the waypoints in order, this will allow us to tell the panda to repeat its actions until all objects have been dropped into the target container.
+
+### init_episode(self, index: int) -> List[str]:
+Upon initialisation of every episode, we will set *self._variation_index* so that we can access it from other member functions.
+
+```python
+self._variation_index = index
+```
+Then we will sample the procedural objects found in the *rlbench/backend/assets/procdural_objects* directory by calling a function from *rlbench/backend/task_utils.py*. As can be seen in this function's definition, we need to pass it a handle to the task_dummy and the number of objects which we wish to spawn. Note that because this call takes place in *init_episode()*, we are sampling a different set of 5 items with each episode call. We will also also declare a variable *bin_objects_not_done* to hold a list of handles to objects not yet emptied out from *large_container*, this will be used later to determine where to set the subsequent positions of the grasping waypoint before each grasp. This snippet ends by registering the sampled objects as graspable, necessary to enable the grasping mechanism explained previously:
+
+```python
+self._variation_index = index
+self.bin_objects = sample_procedural_objects(self.get_base(), 5)
+self.bin_objects_not_done = list(self.bin_objects)
+self.register_graspable_objects(self.bin_objects)
+```
+
+After having done this, we will dynamically spawn our procedural objects in the scene by instantiating our *spawn_boundary* object, and using a for loop to spawn each object. For each object, this will require performing the following operations:
+
+* Placing each object 20cm above *large_container*'s center, then
+* Sampling each object within the *spawn_boundary* in order to drop each object at a random position within the container.
+
+Do this like so:
+
+```python
+b = SpawnBoundary(self.spawn_boundaries)
+for ob in self.bin_objects:
+ ob.set_position(
+ [0.0, 0.0, 0.2], relative_to=self.large_container,
+ reset_dynamics=False)
+ b.sample(ob, ignore_collisions=True, min_distance=0.05)
+```
+
+Next we will create a handle to the release waypoint, and define a list representing a vector of its target position over *small_container0* relative to *large_container*. The X and Y vectors were obtained by temporarily making this waypoint a child of the large container and reading off its position relative to its parent in the *Position/Translation* dialog, as such your X and Y values might differ slightly from the shown here. The Z vector is simply set to +17cm, the desired relative height of *waypoint3*.
+
+```python
+target_waypoint = Dummy('waypoint3')
+target_pos = [-5.9605*10**(-8), -2.5005*10**(-1), +1.7*10**(-1)]
+```
+
+The next few code snippets will deal with selecting and setting the colors of each of the small containers and setting the position of the release dummy *waypoint3*, as well as the registering of success conditions. All of these will depend on the variation (and thus the specific variation index) and so will require first (1) selecting the appropriate parameters and (2) using them to achieve the above points.
+
+We begin by creating an empty list to later hold our success conditions, then choosing the the colors for both the target container and the other distractor container. The color of the target is chosen by simply using *index* to cycle through the const list of colors found in *rlbench/const.py*. We will select the color of the distractor at random by calling *np.random.choice()* on a list containing the indexes of *colors* which excludes the color index of the target, then selecting the chosen color using this index value:
+
+```python
+conditions = []
+target_color_name, target_color_rgb = colors[index]
+color_choice = np.random.choice(
+ list(range(index)) + list(
+ range(index + 1, len(colors))),
+ size=1, replace=False)[0]
+_, distractor_color_rgb = colors[color_choice]
+```
+
+Now we will use a two-pronged if statement to set the color of the *small_container*s, register success conditions, and then select the correct position for *waypoint3*. To determine which container will be the target, we will simply alternate between them by using *index % 2 == 0* to choose *small_container1* and *index % 2 == 1* to choose *small_container1*.
+
+The first part of the if statement thus sets the sets *target_color_rgb* and *distractor_color_rgb* to be the colors of *small_container1* and *small_container2*, respectively, then appends a *DetectedCondition* to the *conditions* for each object. These conditions simply define a succesful task condition as all of our sampled procedural objects being detected the the proximity sensor covering the volume of our target container, *small_container0*. As the target pos of *waypoint3* relative the *large_container* is already correctly defined above, we do not have to alter this. This is written as so:
+
+```python
+if index % 2 == 0:
+ self.target_container0.set_color(target_color_rgb)
+ self.target_container1.set_color(distractor_color_rgb)
+ for ob in self.bin_objects:
+ conditions.append(DetectedCondition(ob, self.success_detector0))
+```
+
+The second prong of our if statement will do almost the same, except the variables containg rgb values to be assigned to each of the small containers will swap, and we will register the proximity sensor corresponding to the other container. We will also set *waypoint3*'s target position relative to *large_container* by changing the sign of the Y value so that it can be placed equivalently on the opposite side of the above the *large_container*, which will result in the release waypoint being placed 17cm above the center of *small_container1* if both small container's are placed perfectly on each side of the large one:
+
+```python
+else:
+ self.target_container1.set_color(target_color_rgb)
+ self.target_container0.set_color(distractor_color_rgb)
+ for ob in self.bin_objects:
+ conditions.append(DetectedCondition(ob, self.success_detector1))
+ target_pos[1] = -target_pos[1]
+```
+
+Next we will set the position of the target_container and register our chosen success conditions as so:
+
+```python
+target_waypoint.set_position(target_pos,
+ relative_to=self.large_container,
+ reset_dynamics=True)
+self.register_success_conditions(
+ [ConditionSet(conditions, simultaneously_met=True)])
+```
+
+Note that here we are registering our success conditions as a *ConditionSet* object. When *simultaneously_met* is set to *True*, this results in identical functionality to simply registering a list, but if we wanted to we could alter the parameters of the *ConditionSet* call to define successful task completion as different conditions met in a certain order, or as conditions which only have to have been met at some point throughout a demo. We conclude *init_episode* by returning an exhaustive list containing the as many differing semantic descriptions of this task as we can think of:
+
+```python
+return ['empty the container in the to %s container'
+ % target_color_name,
+ 'clear all items from the large tray and put them in the %s '
+ 'tray' % target_color_name,
+ 'move all objects from the large container and drop them into '
+ 'the smaller %s one' % target_color_name,
+ 'remove whatever you find in the big box in the middle and '
+ 'leave them in the %s one' % target_color_name,
+ 'grasp and move all objects into the %s container'
+ % target_color_name]
+```
+
+### def variation_count(self) -> int:
+As stated above, the set of possible variations for this task contains each possible color assignment for the target container for each possible target container. This means that we will simply return the following here:
+
+```python
+return 2*len(colors)
+```
+
+### def cleanup(self) -> None:
+As explained in the first walkthrough, state in the V-Rep scene must be preserved between demo runs. As such, any objects instantiated dynamically must be removed from the scene before another demo can be called. This applied to our procedural objects so will loop through them and remove them here. Because we resample a different set of 5 objects with every episode, we will also assign an empty list to *bin_objects* so that it can work with subsequent demo runs:
+
+```python
+def cleanup(self) -> None:
+ if self.bin_objects is not None:
+ [ob.remove() for ob in self.bin_objects if ob.still_exists()]
+ self.bin_objects = []
+```
+
+### def step(self) -> None:
+We will use the step function to handle the list of objects not yet correctly placed. As such, this list should reflect the collection of precedural objects not currently detected by the success detector. As it is not possible to assign a handle to an object handle and then call V-Rep API functions (functions dynamically affecting or reading the state of the V-Rep scene whilst a demo is running) on such handles, we will again use an if statement conditional on *_variation_index % 2* to select the handle to the appropriate proximity sensor. We can itereate through the list of procedural objects which still need to be removed from *large_container* and call *.remove()* to remove them from said list like so:
+
+```python
+for ob in self.bin_objects_not_done:
+ if self._variation_index % 2 == 0:
+ if self.success_detector0.is_detected(ob):
+ self.bin_objects_not_done.remove(ob)
+ else:
+ if self.success_detector1.is_detected(ob):
+ self.bin_objects_not_done.remove(ob)
+```
+
+### def \_move_above_object(self, waypoint):
+This is the function to be called just before any movements/actions are made towards a specified waypoint. We registered this function in *init_task()* and have already passed the grasping waypoint *waypoint1* to to the registering. As such, it is called as soon as *waypoint0* has been reached and any actions specified through extension strings or other means have been performed. We want it to set *waypoint1*'s position to objects which are yet to be removed from *large_container*, as such we will use a getter function to retrieve the position of the first element of *bin_objects_not_done* (this will actually retrieve the position of the central point of an object's bounding box) and then use this to specify *waypoint1*'s position:
+
+```python
+if len(self.bin_objects_not_done) <= 0:
+ raise RuntimeError('Should not be here.')
+x, y, z = self.bin_objects_not_done[0].get_position()
+waypoint.get_waypoint_object().set_position([x, y, z])
+```
+
+### def \_repeat(self):
+The final function to be written simply needs to return a boolean value specifying whether or not we want to repeat the path and actions specified by the waypoints: *True* if objects still need to be placed in the target container and *False* otherwise. We could read the state of the success detector as we did in *step()* but seeing as we already have list representing the objects yet to be moved we can perform a simple comparison:
+
+```python
+return len(self.bin_objects_not_done) > 0
+```
+
+We have now finished creating the scene and python files for this task. Cycle through episodes and run a few demos for a few variations to verify that the task behaves as described throughout this walkthrough. If the task behaves in an unexpected way, or if it fails to succesfully complete a majority of demos, you could play around with the task by playing around with the height of waypoints. The task has been quite narrowly defined throughout this walkthrough, but for tasks you create in general you will likely have to play around with the positions and orientations of waypoints and other objects, their dynamic properties like their massess, extension strings etc, as well as the functions (and parameters) you call in your python file and where you call them from. If your task still fails to achieve the intended behaviour, you may have to redesign the task by remodelling objects and re-thinking your waypoint strategy in the V-Rep simulator, as well as perhaps simplifying your task specification or implementation to reduce its complexity and better handle edge cases. The RLBench and PyRep code bases are your friends here; both provide a wealth of member functions which you can familiarise yourself with by searching through the backend and other files in both repositories, as well as by looking at other tasks which implement functionaility similar to that which your tasks might require. In particular you will want to be aware of the different varieties of success conditions you can register in *rlbench/backend/conditions.py*.
+
+## 4 Passing the Task Validator
+Before your task is complete, every variation must pass the task_validator. For tasks like the one we have created here which have 20 variations which are all almost identical to each other, it is usually sufficient to verify they work for at least 3 variations.
+
+## 5 The Open Road
+Congratulations, you have now completed both tutorials on creating tasks for RLBench. Any tasks you choose to create and add to this dataset will be making a contribution to the research surrounding Machine Learning in Robotics so any input will be greatly appreciated. This is where the fun starts; you can now start thinking creatively about designing your own tasks and you are now armed with the tools you need to build them.
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+# Simple Task Walkthrough
+
+This is a walkthrough of one of the entire task building process of one of the simpler tasks: slide_block_to_target.
+The task aims to make the Panda robot slide a cube to a specific target location and if done right you will end up with something like the following:
+
+
+
+Note that creating a task involves (1) creating the task through the task builder, (2) creating the task by adding objects to the scene in a V-Rep simulator instance, then (3) coding the Python File and finally (4) passing the Task Validator. The last two steps are often an iterative process depending on the complexity of the task. (2) Will require first physical objects to be added, then normally sensors used to detect objects and thus define a succesful task completion, then waypoints used in calculating the Panda Robot's path and action in the simulator. (3) Involves creating handles to various objects and defining what counts as succesful task completion. (4) Must be passed for a task to be deemed a sufficient addition to this data set.
+
+The design of a task must be given careful consideration. Ideally they would be tasks which one can imagine a Panda Arm Robot might one day be asked to do in real life; many tasks built thus far are common household ones such as hanging a picture frame on a wall peg or setting the dinner table, but tasks from construction, manufacturing and any general labour intensive sphere would also be seen as useful. Tasks also have varying levels of complexity and often have common themes tying them together; a good example would be open_cabinet and take_cup_out_from_cabinet. All that said, there really are no hard restrictions so use your creativity and imagination to see what you can create.
+
+
+## Variations and their Episodes
+At this point it is worth talking about episodes and variations. Every task has various textual descriptions asscosciated with them, to be used for linguistic learning in robotic tasks if desired. The picture below shows the various task descriptions returned from the init_episode() function in task *toilet_seat_up* for its single variation. Ideally these will between them include all possible ways of using the English language to describe a task; for example two of the various descriptions given for a particular episode of stack are 'stack the other cups on top of the red cup' and 'pick up and set the cups down into the red cup'.
+
+
+
+Every task has at least one variation, and every variation must have multiple episodes. *stack_cups* has multiple variations for each color of the base cup, for example another one of its variations could be described as 'stack the other cups on top of the blue cup'. Variations aren't restricted to varying colors either; setup_checkers has three episdoes, each instructing the Panda to setup the remaining 1, 2, or 3 checkers on the board.
+
+A *variation* is thus defined as a specific semantic meaning of the textual description of the task, such that different variations actually instruct the robot to do something slightly different on every variation. An *episode* is defined as a specific physical starting state (most likely layout/positioning) of the task objects for a particular variation, and these must differ between episodes across the same variation with the same semantic description. At minimum, the positioning/orientation of task objects must vary with episodes, but many tasks built so far vary object colors and sucess conditions (alongside Panda action instruction) with every change in episode.
+
+## 1 Start the task builder
+First off, simply run the task builder. A new V-Rep instance should automatically open, and this will run from the code found in the RLBench backend and Pyrep repos.
+
+You will then be prompted to enter which task to edit. In this case, enter 'slide_block_to_target' as the task name, press enter, and enter 'y' to when asked to confirm if you want to create a new task. This will prompt the task builder to create both a python file in *rlbench/tasks* and a corresponding scene file with a .ttm extension in *rlbench/task_ttms*. Shown below is the task_buidler dialog for creating a new task called slide_block upon starting the task_builder.
+
+
+
+The ttm file should load automatically in the V-Rep instance, and this will contain the scene (a collection of walls, cameras, a table/workspace boundary, and the panda robot) and a task dummy which should in this case be named 'slide_block_to_target'. Shown below is the empty scene task_design_empty.ttt containing the empty task_dummy object named 'complex_task' for a task also named 'complex_task'.
+
+
+
+Notice in particular the Scene Hierarchy on the left, showing the parent child relationships for all existing objects in the scene, presented in a tree structure. Two things to note here: (1) Be aware that moving/rotating an object will do the same to its child objects, treating them as one single object for these particular operations and (2) it is important that any V-Rep objects created for the task must be made children of this task dummy, and that any objects that exist across tasks as part of the empty scene file (.ttt) are not included in the task dummy's downwards tree. This will ensure that when we save the task it is saved only to the task's scene file (.ttm), and that when we change between tasks everything is cleared away and loaded appropriately. To change the ordering within the scene hierarchy, simply hold down the mouse click and drag the object names up or down to the desired new parent and then release the mouse click.
+
+
+## 2 Creating the Task in V-REP
+To create a task, it must first be designed in the RLBench V-Rep instance. We will create the needed objects, making sure to set the dynamic and object special properties. For this task, a cube cube named 'block' was added along with a plane named 'target'. These objects need to placed on top of the table, within the 'workspace' boundary in order to make sure the panda can reach the objects and complete the task.
+
+Note first that in order to pan your view in the V-Rep instance you can make sure that the pan icon is selected in the upper left section of the screen (see picture below) then pan your view in a drag and drop fashion. To rotate your view angle, either hold down the scroller wheel button on your mouse if available, or select the icon directly to the right of the camera pan icon and move it in a drag and drop fashion. Spend a few seconds getting comfortable with this as creating a task will involve needing to view various positions from all sorts of angles.
+
+
+
+To create the block, go to the top toolbar and click on Add -> Primitive Shape -> Cuboid. You will then be prompted with a dialog where you can initialise the cuboid's properties:
+
+
+
+For now let's ensure the cuboid dimensions are 0.1m along each edge, and click ok to create it. Notice that this cuboid is grey in color and is spawned on the floor underneath the table. Seeing as we will be setting the colour of the block dynamically from the Python file side there will be no need to do that just yet, but be aware that we would normally adjust the color of created objects as shown below. This is done by double clicking on the Cuboid in the V-Rep scene heirarchy to open the Scene Object Properties dialog, ensuring we are viewing the 'Shape' rather than 'Common' Properties, and clicking on either 'Adjust color' or 'Adjust texture'/'Quick textures (selection)'.
+
+
+
+Once our cuboid has been created, we need to place it on top of the table. There are multiple ways to do this, here we will simply drag and drop it. In order to be able to to do this, we need to select the Translation/Position dialog by clicking on its icon in the upper left section of the V-Rep instance. We can select multiple ways of controlling an objects position; to select drag and drop click on the 'Mouse Translation' tab. From here we can select the axes along which to translate, selecting only 'along Z' as this will allow us to adjust its vertical position as shown in the figure below. By dragging it upwards we can place it on top of the table, noting that it is not a problem for there to be a (very) miniscule gap between the object and the table. To reduce the step size of the translation, initiate the translation by holding down the left mouse click, then keep the shift key pressed as this will allow you to place the object more precisely. Note also that to move objects along other axes or planes we can simply select the desired axes of translation as so, and that sometimes we want to move objects along axes relative to other objects if thieir bounding box has an awkward orientation (more on this in another tutorial).
+
+
+
+We finish creating the cuboid by setting its *Object Special Properties*. Double click on the cuboid in the Scene Hierarchy to open its Object Special Properties Dialog and open the *Common* tab. Under *Object special properties* set the object to *Collidable*, *Measurable*, *Detectable* and *Renderable* by checking their respective boxes:
+
+
+
+*Collidable* means that the V-Rep simulator is able to detect a collision with other objects, and this is particular useful for path planning. *Measurable* enables minimum distance calculation between two objects and this we normally want to set this to enabled. *Detectable* enables detection of the object by proximity sensors; we will add one later in this task. Dinally *Renderable* means the object can be rendered by cameras and we also want this enabled for almost every object so that an object is visually present at training time.
+
+Next we will create the target_visual. Create a plane by clicking on Add -> Primitive Shape -> Plane and create a plane with sides of length 0.1m. Set all four of its *Object special properties*. As we have done for the cuboid, drag and drop this onto the surface of the table, placing it next to the cuboid with roughly 0.1m of seperation. In this example, the cuboid is colored red and the plane is white, but you only need to worry about giving the plane a color so that it can be rendered as a surface and not just a hollow collection of edges, also known as a wireframe. At this point you should have something similar to the following picture:
+
+
+
+We colored in our plane white, but only parts of the plane appear colored from most viewpoints, the rest are not visible. You will notice that if the plane has the same vertical position as the surface of the table, it may or may not be wholly visible depending on the angle it is viewed from. For this reason, we will use the translation dialog to increase its z height by a miniscule amount. Open the Translation/Position dialog and go the 'Translation' tab. Enter *+1.00e-5* in the *Along Z* box and click on the *Z-translate sel.* button. Your plane should now be placed just above the surface of the table, and its colored surface visible from all direct viewpoints.
+
+
+
+Now that we have the necessary physical objects placed appropriately in the scene, we need to add a proximity sensor. This will be needed to detect if the block has reached the target and will be used to define a succesful task completion later when writing the python file. Click on Add -> Proximity Sensor -> Pyramid Type. This will create a sensor with a detection volume far bigger than we need, so double click on the Proxity_sensor in the Scene Hierarchy to open its Scene Object Properties, click on *Show volume parameters* to open the Detection Volume Parameters dialog, and fill the fields in as shown below. This will drastically reduce the size like so:
+
+
+
+We want the task to slide the cuboid to the plane, as such we will place the proximity sensor at the centre of the plane. The easiest way to do this is to first make the sensor a child of the plane: drag and drop it onto the plane in the scene hierarchy, open the Translation/Position dialog, click on the *Position* tab, and then set its position relative to its parent frame to 0 along all axes like below. Rename this sensor to *success*.
+
+
+
+Next we need to deal with the physical properties of our objects. Both the block and the target have all Object Special Properties (Collidable, Measureable, Detectable, Renderable) set, and the block needs to be set to both respondable and dynamic within the dynamic properties log. *Respondable* means the object has a physical presence, ie that it exists for the purposes of physical interaction with other objects in the scene. *Dynamic* means external forces other than those applied to it by other forces can act on it, ie gravity and reactionary force. For both block, open its Scene object properties dialog, ensure the *Shape* tab is open, then click on *Show dynamic properties dialog*. This will open the Rigid Body Dynamic Properties dialog, check the boxes correspnding to *Body is respondable* and *Body is dynamic*.
+
+Next, dummies named *waypoint0* and *waypoint1* need to be added. Dummies named in this way act as much needed waypoints in defining the intended motion and action of the panda gripper, and will be used by the path planning script in RlBench to determine what the panda does in simulation. The intended orientation of the dummies must be set appropriately, and in the next tutorial it will be seen that dummies can have extension_strings assosciated with them, instructing the gripper to do more complex things like grasping and ignoring collisions in path planning. Note that the panda gripper's motion and action will follow first waypoint0, then waypoint1 and so on in an ascending order.
+
+To add such dummies, create them by clicking on Add -> Dummy. Make it a child of the Proximity Sensor and set its position relative it its parent frame to 0 along all axes, renaming this dummy as 'waypoint0' by double clicking on its name in the Scene Hierarchy, typing it, and and hitting enter. Add another Dummy and set it to a child of the cuboid, then open the Translation/Position dialog, set its relative position (to parent) to 0m in the X-coord and Z-coord boxes, but set the Y-coord box to -0.1m such that it is placed on the other side of the cuboid as the plane and proximity sensor. Name this 'waypoint1'.
+
+At this point, we have a block sitting between 'waypoint0' and 'waypoint1', and hence Panda ends up planning a path that goes around the block. To avoid this, we must assign extension strings to 'waypoint1' by opening its Scene Object Properties dialog, select the Common tab at the top of the dialog, and typing in 'ignore_collisions()' in the Extension String text box found under General properties.
+Finally we need to set the orientation of the waypoint dummys so that the panda has the easiest possible path to plan; this is because in simulation the panda will orient its gripper such that the dummy named 'Panda_tip' (which maintains constant pose relative to the Panda Tip) has an identical orientation to the waypoints it is reaching, and care must be taken to ensure that these waypoints have a feasible (ie reachable) pose. Open the Object/Item Rotatation/Orientation dialog by clicking on the icon just to the right of the Position/Translation dialog, go the the *Orientation* tab, and fill in its orientation (relative to the world) like so:
+
+
+
+Finally, a plane named 'boundary' was created. This is defined to randomly spawn (move and rotate) the task objects within itself, and is needed for when we code the python file later. To create this we could simply add a plane of desired geometry and drag it onto the table surface, but there is an easier way. Select the workspace in the Scene hierarchy, do Ctrl+c then Ctrl+v to copy and paste it, then double click on the newly created workspace0 in the Scene Hierarchy to open its Scene Object Properties dialog. Click on *View/modify geometry* to open its geometry dialog, uncheck the *Keep proportions* box, and change the X and Y properties under *Scaling factors* to 0.70, keeping Z as 1.00. Click apply; this should leave you with a smaller *workspace0* inside than the workspace boundary, and take care to ensure that all objects which we have created are positioned within this new boundary.
+
+
+
+Remember to make *workspace0* a child of the slide_block_to_target task dummy by dragging and dropping it onto the task dummy within the scene hierarchy. Now we need to rename the shapes so that we can create handles with maximum readability for them later on in the Python file. Rename the Cuboid to *block*, the proximity sensor to *success*, workspace0 to *boundary* and the Plane to *target*. Finally, ensure all objects we created are children of the target plane, as later on when we define random spawning of the plane all child objects will be keep their pose relative to their parent constant. Ensure that your parent-child hierarchy is as so:
+
+
+
+## 3 Writing the Python File
+Now that we have created our simple scene file, we find and open *rlbench/tasks/slide_block_to_target.py*. This will contain empty functions as shown below, most of which must be defined at this point.
+
+```python
+from typing import List
+from rlbench.backend.task import Task
+
+
+class ComplexTask(Task):
+
+ def init_task(self) -> None:
+ # TODO: This is called once when a task is initialised.
+ pass
+
+ def init_episode(self, index: int) -> List[str]:
+ # TODO: This is called at the start of each episode.
+ return ['']
+
+ def variation_count(self) -> int:
+ # TODO: The number of variations for this task.
+ return 1
+
+ def step(self) -> None:
+ # Called during each sim step. Remove this if not using.
+ pass
+
+ def cleanup(self) -> None:
+ # Called during at the end of each episode. Remove this if not using.
+ pass
+```
+
+Explained, these are:
+
+* **init_task()** - This is called whenever the simulator is started, and is called before any other functions contained in this file.
+* **init_episode()** - This is called before the start of each indivual demo, it will automatically sample the task dummy and thus any children it has within the workspace whilst keeping the relative pose between all task objects we have created constant, but the rest is left up to the task creator.
+* **variation_count()** - This must return the total number of variations, necessary for automating the running of this data set.
+* **step()** - This is called at each time step in simulation, and can be used for altering the state of the scene once certain conditions have been met, for example the button in push_button changes color from red to green once succesfully pressed. This function is optional and should be deleted when not used.
+* **cleanup()** - This is used to reset the state of the scene in between each run, it is called upon the completion of any succesful or unsuccesful demo. If your task alters the set of objects within the scene, ie by creating or deleting objects dynamically, the scene must be restored to its original state here. For example, in wipe_desk, the dirt spots dynamically created and spawned at the beggining of each demo must be deleted. This should also be deleted when not being used.
+
+We first ensure that we import the necessary functions and classes from the pyrep and rlbench backend, the ones ones listed here are typical of most tasks and include standard classes Shape and Task, as well as classes interacting with the Spawn Boundary and Proximity Sensor, and finally a list defining colours in RGB format and a definition for the success condition to be used in this task:
+
+```python
+from typing import List
+from pyrep.objects.shape import Shape
+from pyrep.objects.proximity_sensor import ProximitySensor
+from rlbench.const import colors
+from rlbench.backend.task import Task
+from rlbench.backend.conditions import DetectedCondition
+from rlbench.backend.spawn_boundary import SpawnBoundary
+```
+
+### init_task() -> None:
+Next we need to complete the functions instantiated in the SlideBlockToTarget task. The init_task() function will be called once when the simulator is started (by entering '+' in the task_builder dialog) and will need to contain our initialisation of various types of object handles, as well as the initialisation of success conditions that will remain constant throughout different episodes. Note that a demo of a task ends once all success conditions have been reached, these will include conditions on the state of the gripper (ie whether or not it is currently grapsing), as well as conditions on a particular proximity sensor detecting a particular object. More complex tasks will have success conditions varying and re-registering with each new episode and these will thus be dealt with inside the init_episode() function.
+
+In order to instiate an object handle, simply create it using existing classes. Most objects, including boundary planes, will be a simple Shape, but other classes like spawn boundaries and proximity sensors will need to be created as such:
+
+```python
+self.block = Shape('block')
+success_detector = ProximitySensor('success')
+self.target = Shape('target')
+self.boundary = SpawnBoundary([Shape('boundary')])
+```
+
+The last step to create init_task for this task will be to instatiate the success condition as a DetectedCondition() class, passing first the handle to the object to be detected and the intended proximity sensor, and then these success conditions will be registered, instructing the task_builder to stop and declare a succesful task once this condition has been reached. Notice that these conditions must be passed as a list, allowing multiple conditions to be ANDed together to define succesful task completion:
+
+```python
+success_condition = DetectedCondition(self.block, success_detector)
+self.register_success_conditions([success_condition])
+```
+
+### init_episode(self, index: int) -> List[str]:
+If the task is to have more than 1 variation, this function must first set *self._variation_index* for as below. Be aware for when creating your own tasks that this *index* variable is the only available information pertaining to which variation the task is currently set to perform. As such, any differences in your python script execution flow between variations must be dependent on this variable. This will, however, be illustrated in a more advanced tutorial so you do not need to worry about this for now.
+
+```python
+self._variation_index = index
+```
+
+On calling a new episode, RLBench backend will randomly sample the task_dummy, along with all of its children, within the workspace boundary, keeping their relative positions and orientatations constant. In order to add more task variations to the dataset, we can cycle through the coloring of objects and textual descriptions (more on this later) of tasks by having these variations depend on the index. Here we cycle through block colours by choosing the desired colour from the pre-exisiting 'colors' list and calling .set_color on the block:
+
+```python
+block_color_name, block_rgb = colors[index]
+self.block.set_color(block_rgb)
+```
+
+Next we clear our boundary and call .sample() on it in order to randomly move and rotate the task objects within it. Notice that only self.target needs to be passed in order to sample all task objects together whilst keeping their relative positions in tact, this is because all task objects (excluding the boundary) are children of self.target:
+
+```python
+self.boundary.clear()
+self.boundary.sample(self.target)
+```
+
+We finish writing init_episode by defining its return. This must be a list of task descriptions written in english, to be used for semantic learning. Notice the *%s* string placeholder, this is done because the specific value of that string (aka the block color) will vary with every variation and is determined by the self.variation_index variable seen above. Make sure to return a list containing as many textual descriptions as you can think of, as this will massively aid this dataset's usefulness to robotic linguistic learning.
+
+```python
+return ['slide the %s block to target' % block_color_name,
+ 'push the %s cube to the white plane' % block_color_name,
+ 'nudge the %s block so that it covers the white target' % block_color_name,
+ 'Find the %s item on the table and manipulate its position so that it reaches the plane' % block_color_name]
+```
+
+### variation_count(self) -> int:
+Finally, every task needs to have variation_count() defined. This must return the number of distinct task variations. In the case of slide_block_to_target, each variation will set the block colour to a different element from the colors list. As such, this function will simply return its length:
+
+
+```python
+return len(colors)
+```
+
+### Other class functions
+Any other functions not being used, such as cleanup(), can be deleted in order to achieve a readable python file.
+
+## 4 Playing around with the Task
+Once the scene and python files have been created, it is time to test the task runs. This can be done by entering '+' in the task builder dialog to run the simulator, then 'd' to test the demo of this particular variation. We are looking to make sure that the panda can reach most spawning positions across episodes and that the task works for all variations, this can be done by running demos for different episodes (press 'e' to cycle) and variations (press 'v') respectively. Common (but non-exhaustive) things to change at this point include changing the position/orientation/size of objects to make them reachable/graspable by the gripper, changing the position/orientation/size of any spawn boundaries to ensure most variations run smoothly, changing the min_rotation and max_rotation (through altering the .sample() parameters for any spawn boundary sampling and by redifining base_rotation_bounds() for workspace sampling on init_episode() calls), changing the dynamic properties (inc mass), and tinkering with the waypoint dummys to ensure a smooth and straightfoward path can be consistently planned.
+
+## 5 Passing the Task Validator
+To ensure a task has been made to completion, it must pass the task validator. This can be done by passing '!' to the task_builder dialog when not in simulation, and the task validator must deem the task succesful for demos it runs across every distinct variation. Most demos must be succesfully passed for the task validator to declare a succesful task. If your task does not pass this, go back to Step 4 and try again. If this still proves to be a major challenge, redesign and recreate the task in a simpler fashion.
+
+## 6 What's Next?
+You should now be able to understand how the simpler tasks in RlBench were created. More complicated tasks involve more complex code, for example in changing the position of waypoints dynamically whilst a demo is running, spawning and then deleting objects dynamically, changing object properties such as colour within the task etc. Once you have understood how this task was created, work your way through the next tutorial on creating a more complex task to familiarise yourself with how these problems have been solved in examples created so far.
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