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tutorials:advanced:unreal [2020/01/13 14:13] – [Importing new episode data into MongoDB and KnowRob(Additional information)] hawkintutorials:advanced:unreal [2020/01/13 14:29] – [Idea] added idea description hawkin
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 This tutorial will introduce you to the ''cram_knowrob_vr (short: kvr)'' package, which uses the data recorded in the Virtual Reality environment using [[http://robcog.org/|RobCog]], extracts information from them using [[http://www.knowrob.org/|KnowRob]] and executes the CRAM high-level-plans based on this data either on the real robot or in the CRAM [[tutorials:advanced:bullet_world|bullet world]]. This tutorial will introduce you to the ''cram_knowrob_vr (short: kvr)'' package, which uses the data recorded in the Virtual Reality environment using [[http://robcog.org/|RobCog]], extracts information from them using [[http://www.knowrob.org/|KnowRob]] and executes the CRAM high-level-plans based on this data either on the real robot or in the CRAM [[tutorials:advanced:bullet_world|bullet world]].
  
 +==== Idea ====
 +We want to essentially teach a robot how to perform every day activities without having to dive deep into code, but rather simply show the robot what we want him to do using Virtual Reality. This would allow robots to learn from humans easily, since this way, the robot can acquire information about where the human was looking for things and where things were placed. Of course, this could also be hard coded, but that would take a lot more time and be prone to failure, since we as humans, often forget to describe very minor things which we automatically take for granted, but which can play a huge role in the success of a task for the robot. E.g. if the cooking pot is not in its designated area, we would automatically check the dishwasher or the sink area. This is something the robot would have to learn first.
 +
 +The advantage of using Virtual Reality for this is also, that we can train the robot on all kinds of different kitchen setups, which can be build within a few minutes, instead of having to move around physical furniture. This would also allow for generalization of the acquired data and would add to the robustness of the pick and place tasks. 
 ==== Prerequisites ==== ==== Prerequisites ====
 This tutorial assumes that you've completed the [[tutorials:intermediate:json_prolog|Using JSON Prolog to communicate with KnowRob]] tutorial and therefore have ROS, CRAM, KnowRob and MongoDB installed. In order to be able to use the kvr package, a few specific changes have to be made. Within the ''knowrob_addons'' the ''knowrob_robcog'' package has to be replaced by this one [[https://github.com/robcog-iai/knowrob_robcog.git This tutorial assumes that you've completed the [[tutorials:intermediate:json_prolog|Using JSON Prolog to communicate with KnowRob]] tutorial and therefore have ROS, CRAM, KnowRob and MongoDB installed. In order to be able to use the kvr package, a few specific changes have to be made. Within the ''knowrob_addons'' the ''knowrob_robcog'' package has to be replaced by this one [[https://github.com/robcog-iai/knowrob_robcog.git
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 === Performance === === Performance ===
 +This step is also covered by the [[https://github.com/hawkina/useful_scripts | scripts]] mentioned above, but can also be executed manually. 
 +
 Depending on how many collections your database has, it can get slow when quering for information. One way to make it faster, is to include an index over timestamp for all collections. One way to add this, is to install [[https://www.mongodb.com/products/compass|compass]] for mongodb. Launch it, and connect it to your database. The defautl settings should be fine so just click ''ok'' when it launches. Then go to your collection -> indexes -> create index. Call the new index ''timestamp'', select the field ''timestamp'' and set the type to ''1 (asc)'', click create. Repeat for all the collections. It will improve the query speed greatly. Depending on how many collections your database has, it can get slow when quering for information. One way to make it faster, is to include an index over timestamp for all collections. One way to add this, is to install [[https://www.mongodb.com/products/compass|compass]] for mongodb. Launch it, and connect it to your database. The defautl settings should be fine so just click ''ok'' when it launches. Then go to your collection -> indexes -> create index. Call the new index ''timestamp'', select the field ''timestamp'' and set the type to ''1 (asc)'', click create. Repeat for all the collections. It will improve the query speed greatly.