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License: MIT License
VU-CLTL Pepper/Nao Application Repository (Python 2)
License: MIT License
"One utterance to many triples"
Currently, the triples generated contain entities extracted as "lenka-sister" (eg. lenka-sister_is-from_serbia), which might need to be broken down into triples themselves (lenka_has-sister_x and x_is-from_serbia)
This can be noted through the litst of types extracted. Usually when the list of types for a single entity contains certain combinations (eg. adjective and noun)
The Wolfram Responder is throwing an error regarding number of arguments during init.
Hey,
I manually tested the language test scenarios in a live application: worked through them all and looked if they were working. All entries with a tick box are working! For every entry that is not working 100%, the log is provided. Hope this is useful for debugging :)
These tests were done on the latest Develop branch!
Here we go:
10/16/19 13:25:16 INFO pepper.Chat (Bram) 006 Bram: "who is the robot"
10/16/19 13:25:16 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'is', 'object': u'the-robot', 'subject': ''}
10/16/19 13:25:16 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'be', 'object': u'the-robot', 'subject': ''}
10/16/19 13:25:16 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'be', 'object': u'the-robot', 'subject': ''}
10/16/19 13:25:16 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'be', 'type': u'verb.stative'}, 'object': {'text': u'the-robot', 'type': [u'noun.artifact']}, 'subject': {'text': '', 'type': []}} {'neg': False, 'wh_word': {u'response': u'person'}}
10/16/19 13:25:16 INFO pepper.pepper.language.analyzer RDF subject: {"text": "", "type": []}
10/16/19 13:25:16 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "be", "type": "verb.stative"}
10/16/19 13:25:16 INFO pepper.pepper.language.analyzer RDF object: {"text": "the-robot", "type": ["noun.artifact"]}
10/16/19 13:25:16 DEBUG pepper.RdfBuilder Booted
10/16/19 13:25:17 DEBUG pepper.BrainResponder TRIPLE: ?_be_the-robot [_->_artifact or object])
10/16/19 13:25:17 INFO pepper.LongTermMemory Triple: ?_be_the-robot [_->_artifact or object])
10/16/19 13:25:17 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:25:17 DEBUG pepper.BrainResponder REPLY: I know things usually be artifact, but I do not know this case
10/16/19 13:25:17 INFO pepper.ResponsePicker BrainResponder (0.18s)
10/16/19 13:25:17 INFO pepper.Chat (Bram) 007 Leolani: "I know things usually be artifact, but I do not know this case"
works, but does not form a reply, resulting in another responder to be called.
10/16/19 13:26:06 INFO pepper.Chat (Bram) 014 Bram: "you live in Amsterdam"
10/16/19 13:26:06 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'live', 'object': u'in-Amsterdam', 'subject': u'you'}
10/16/19 13:26:06 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'live', 'object': u'in-Amsterdam', 'subject': u'you'}
10/16/19 13:26:06 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'live', 'object': u'in-Amsterdam', 'subject': u'leolani'}
10/16/19 13:26:06 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'live-in', 'type': [u'verb.stative', 'preposition']}, 'object': {'text': u'Amsterdam', 'type': u'noun.location'}, 'subject': {'text': u'leolani', 'type': ['robot']}} {'neg': False}
10/16/19 13:26:06 INFO pepper.pepper.language.analyzer RDF subject: {"text": "leolani", "type": ["robot"]}
10/16/19 13:26:06 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "live-in", "type": ["verb.stative", "preposition"]}
10/16/19 13:26:06 INFO pepper.pepper.language.analyzer RDF object: {"text": "Amsterdam", "type": "noun.location"}
10/16/19 13:26:06 DEBUG pepper.RdfBuilder Booted
10/16/19 13:26:06 DEBUG pepper.BrainResponder TRIPLE: leolani_live-in_amsterdam [robot_->_location])
10/16/19 13:26:06 DEBUG pepper.LocationReasoner Posting query
10/16/19 13:26:06 INFO pepper.LongTermMemory Triple: leolani_live-in_amsterdam [robot_->_location])
10/16/19 13:26:06 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:06 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:06 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:06 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:06 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 INFO pepper.LongTermMemory Posting triples
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:26:07 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:26:07 DEBUG pepper.BrainResponder REPLY:
10/16/19 13:26:07 INFO pepper.ResponsePicker GreetingResponder (0.00s)
10/16/19 13:26:07 INFO pepper.Chat (Bram) 015 Leolani: "What's new?, Bram!"
who lives in Amsterdam
do you live in Amsterdam
I am a man
again: works, but does not form a reply, resulting in another responder to be called.
10/16/19 13:27:01 INFO pepper.Chat (Bram) 024 Bram: "I am a man"
10/16/19 13:27:01 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'am', 'object': u'a-man', 'subject': u'I'}
10/16/19 13:27:01 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'be', 'object': u'a-man', 'subject': u'I'}
10/16/19 13:27:01 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'be', 'object': u'a-man', 'subject': 'Bram'}
10/16/19 13:27:01 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'be', 'type': u'verb.stative'}, 'object': {'text': u'a-man', 'type': [u'noun.person']}, 'subject': {'text': 'Bram', 'type': ['person']}} {'neg': False}
10/16/19 13:27:01 INFO pepper.pepper.language.analyzer RDF subject: {"text": "Bram", "type": ["person"]}
10/16/19 13:27:01 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "be", "type": "verb.stative"}
10/16/19 13:27:01 INFO pepper.pepper.language.analyzer RDF object: {"text": "a-man", "type": ["noun.person"]}
10/16/19 13:27:01 DEBUG pepper.RdfBuilder Booted
10/16/19 13:27:01 DEBUG pepper.BrainResponder TRIPLE: bram_be_a-man [person_->_person])
10/16/19 13:27:01 DEBUG pepper.LocationReasoner Posting query
10/16/19 13:27:01 INFO pepper.LongTermMemory Triple: bram_be_a-man [person_->_person])
10/16/19 13:27:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 INFO pepper.LongTermMemory Posting triples
10/16/19 13:27:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:27:02 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:27:02 DEBUG pepper.BrainResponder REPLY:
10/16/19 13:27:02 INFO pepper.ResponsePicker UnknownResponder (0.00s)
10/16/19 13:27:02 INFO pepper.Chat (Bram) 025 Leolani: "Why?"
who is a man
I like baking cakes
what do I like
do I like baking cakes
who likes baking cakes - you told me you like baking cakes
Birds can sing
The <object> part of the triple is empty? Resulting in strange reply!
10/16/19 13:28:09 INFO pepper.Chat (Bram) 036 Bram: "birds can sing"
10/16/19 13:28:09 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'can', 'object': u'sing', 'subject': u'birds'}
10/16/19 13:28:09 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'can', 'object': u'sing', 'subject': u'birds'}
10/16/19 13:28:09 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'can', 'object': u'sing', 'subject': u'birds'}
10/16/19 13:28:09 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'can-sing', 'type': [u'verb.creation']}, 'object': {'text': '', 'type': []}, 'subject': {'text': u'birds', 'type': u'noun.animal'}} {'neg': False}
10/16/19 13:28:09 INFO pepper.pepper.language.analyzer RDF subject: {"text": "birds", "type": "noun.animal"}
10/16/19 13:28:09 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "can-sing", "type": ["verb.creation"]}
10/16/19 13:28:09 INFO pepper.pepper.language.analyzer RDF object: {"text": "", "type": []}
10/16/19 13:28:09 DEBUG pepper.RdfBuilder Booted
10/16/19 13:28:09 DEBUG pepper.BrainResponder TRIPLE: birds_can-sing_? [animal_->_])
10/16/19 13:28:09 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:09 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:12 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:12 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:12 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:12 DEBUG pepper.TypeReasoner Posting query
10/16/19 13:28:12 DEBUG pepper.LocationReasoner Posting query
10/16/19 13:28:12 INFO pepper.LongTermMemory Triple: birds_can-sing_? [animal_->_])
10/16/19 13:28:12 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:12 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:12 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:12 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:12 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 INFO pepper.LongTermMemory Posting triples
10/16/19 13:28:13 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:28:13 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:28:13 DEBUG pepper.BrainResponder REPLY: Exciting news! I had never heard about before!
10/16/19 13:28:13 INFO pepper.ResponsePicker BrainResponder (4.45s)
10/16/19 13:28:13 INFO pepper.Chat (Bram) 037 Leolani: "Exciting news! I had never heard about before!"
no answer from brain
10/16/19 13:28:23 INFO pepper.Chat (Bram) 038 Bram: "what can birds do"
10/16/19 13:28:23 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'can-do', 'object': '', 'subject': u'birds'}
10/16/19 13:28:23 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'can', 'object': '', 'subject': u'birds'}
10/16/19 13:28:23 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'can', 'object': '', 'subject': u'birds'}
10/16/19 13:28:23 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'can', 'type': None}, 'object': {'text': '', 'type': []}, 'subject': {'text': u'birds', 'type': u'noun.animal'}} {'neg': False, 'aux': {u'person': [u'first', u'second', u'third'], u'tense': u'present', u'number': [u'singular', u'plural']}, 'wh_word': {u'response': u'object'}}
10/16/19 13:28:23 INFO pepper.pepper.language.analyzer RDF subject: {"text": "birds", "type": "noun.animal"}
10/16/19 13:28:23 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "can", "type": null}
10/16/19 13:28:23 INFO pepper.pepper.language.analyzer RDF object: {"text": "", "type": []}
10/16/19 13:28:23 DEBUG pepper.RdfBuilder Booted
10/16/19 13:28:23 DEBUG pepper.BrainResponder TRIPLE: birds_can_? [animal_->_])
10/16/19 13:28:23 INFO pepper.LongTermMemory Triple: birds_can_? [animal_->_])
10/16/19 13:28:23 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:28:23 DEBUG pepper.BrainResponder REPLY: I know animal usually can things, but I do not know this case
10/16/19 13:28:23 INFO pepper.ResponsePicker BrainResponder (0.13s)
10/16/19 13:28:23 INFO pepper.Chat (Bram) 039 Leolani: "I know animal usually can things, but I do not know this case"
who can sing
Robots can't eat food
Think all goes well here, because {'neg': True}
But again, no reply is formed, resulting in another responder to respond
10/16/19 13:29:01 INFO pepper.Chat (Bram) 044 Bram: "robots can't eat food"
10/16/19 13:29:01 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'can-not-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:01 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'can-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:01 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'can-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:01 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'can-eat', 'type': [u'verb.consumption']}, 'object': {'text': u'food', 'type': u'noun.Tops'}, 'subject': {'text': u'robots', 'type': u'noun.artifact'}} {'neg': True}
10/16/19 13:29:01 INFO pepper.pepper.language.analyzer RDF subject: {"text": "robots", "type": "noun.artifact"}
10/16/19 13:29:01 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "can-eat", "type": ["verb.consumption"]}
10/16/19 13:29:01 INFO pepper.pepper.language.analyzer RDF object: {"text": "food", "type": "noun.Tops"}
10/16/19 13:29:01 DEBUG pepper.RdfBuilder Booted
10/16/19 13:29:01 DEBUG pepper.BrainResponder TRIPLE: robots_can-eat_food [artifact or object_->_Tops])
10/16/19 13:29:01 DEBUG pepper.LocationReasoner Posting query
10/16/19 13:29:01 INFO pepper.LongTermMemory Triple: robots_can-eat_food [artifact or object_->_tops])
10/16/19 13:29:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:01 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:01 INFO pepper.LongTermMemory Posting triples
10/16/19 13:29:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:02 DEBUG pepper.ThoughtGenerator Posting query
10/16/19 13:29:02 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:29:02 DEBUG pepper.BrainResponder REPLY:
10/16/19 13:29:02 INFO pepper.ResponsePicker UnknownResponder (0.00s)
10/16/19 13:29:02 INFO pepper.Chat (Bram) 045 Leolani: "I'm guessing you told me: robots can't eat food, but I have no clue!"
Pepper thinks robot CAN eat food, while I just told her robots cannot.
Observe that {'neg': False}
10/16/19 13:29:21 INFO pepper.Chat (Bram) 046 Bram: "can robots eat food"
10/16/19 13:29:21 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'can-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:21 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'can-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:21 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'can-eat', 'object': u'food', 'subject': u'robots'}
10/16/19 13:29:21 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'can-eat', 'type': [u'verb.consumption']}, 'object': {'text': u'food', 'type': u'noun.Tops'}, 'subject': {'text': u'robots', 'type': u'noun.artifact'}} {'neg': False}
10/16/19 13:29:21 INFO pepper.pepper.language.analyzer RDF subject: {"text": "robots", "type": "noun.artifact"}
10/16/19 13:29:21 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "can-eat", "type": ["verb.consumption"]}
10/16/19 13:29:21 INFO pepper.pepper.language.analyzer RDF object: {"text": "food", "type": "noun.Tops"}
10/16/19 13:29:21 DEBUG pepper.RdfBuilder Booted
10/16/19 13:29:21 DEBUG pepper.BrainResponder TRIPLE: robots_can-eat_food [artifact or object_->_Tops])
10/16/19 13:29:21 INFO pepper.LongTermMemory Triple: robots_can-eat_food [artifact or object_->_Tops])
10/16/19 13:29:21 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:29:21 DEBUG pepper.BrainResponder REPLY: you told me Robots can eat food
10/16/19 13:29:21 INFO pepper.ResponsePicker BrainResponder (0.15s)
10/16/19 13:29:21 INFO pepper.Chat (Bram) 047 Leolani: "you told me Robots can eat food"
Same problem as above, the negation doesn't come through
10/16/19 13:29:29 INFO pepper.Chat (Bram) 048 Bram: "who can eat food"
10/16/19 13:29:29 DEBUG pepper.pepper.language.analyzer initial RDF: {'predicate': u'can-eat', 'object': u'food', 'subject': ''}
10/16/19 13:29:29 DEBUG pepper.pepper.language.analyzer after VP: {'predicate': u'can-eat', 'object': u'food', 'subject': ''}
10/16/19 13:29:29 DEBUG pepper.pepper.language.analyzer after NP: {'predicate': u'can-eat', 'object': u'food', 'subject': ''}
10/16/19 13:29:29 DEBUG pepper.pepper.language.analyzer final RDF: {'predicate': {'text': u'can-eat', 'type': [u'verb.consumption']}, 'object': {'text': u'food', 'type': u'noun.Tops'}, 'subject': {'text': '', 'type': []}} {'neg': False, 'wh_word': {u'response': u'person'}}
10/16/19 13:29:29 INFO pepper.pepper.language.analyzer RDF subject: {"text": "", "type": []}
10/16/19 13:29:29 INFO pepper.pepper.language.analyzer RDF predicate: {"text": "can-eat", "type": ["verb.consumption"]}
10/16/19 13:29:29 INFO pepper.pepper.language.analyzer RDF object: {"text": "food", "type": "noun.Tops"}
10/16/19 13:29:29 DEBUG pepper.RdfBuilder Booted
10/16/19 13:29:29 DEBUG pepper.BrainResponder TRIPLE: ?_can-eat_food [_->_Tops])
10/16/19 13:29:29 INFO pepper.LongTermMemory Triple: ?_can-eat_food [_->_Tops])
10/16/19 13:29:29 DEBUG pepper.LongTermMemory Posting query
10/16/19 13:29:29 DEBUG pepper.BrainResponder REPLY: you told me Robots can eat food
10/16/19 13:29:29 INFO pepper.ResponsePicker BrainResponder (0.14s)
10/16/19 13:29:29 INFO pepper.Chat (Bram) 049 Leolani: "you told me Robots can eat food"
Use scene classification from: https://github.com/IBM/MAX-Scene-Classifier
Available as Docker
The outcome of analysing an utterance generates a triple representation, which includes types for any detected entities. These types could come from the internal parsing mechanisms and are not always useful. An extensive analysis on the input for triple representation is needed to filter out unnecessary noise.
How to install openface docker on windows 7 ?
Please also mention the detailed instruction to run this repo.
Migrate to Facenet, as it is the current and stable state-of-the-art
Use pose recognition from:
Available as pre-trained model: https://github.com/tensorflow/tfjs-models/tree/master/posenet#loading-a-pre-trained-posenet-model
Available as Docker: https://github.com/deephdc/posenet-tf#docker-installation
Currently, a trust value for the speaker is computed on every parsed utterance. Since trust computation is expensive (6 queries), producing a response is time-consuming and the robot behaviour is severely affected.
The trust computation should be moved to be computed across the network, on initialization/call.
The Location responder has the ability to set the name of a location given a cue, but it does not make use of the brain ability to reason about previous experiences to guess the location.
In this class
pepper/pepper/responder/sensory.py
Line 181 in 233d39d
pepper/pepper/responder/sensory.py
Line 203 in 233d39d
then call
and set the label, checking for None. For an example see:
We need to formally express that two entities are actually the same thing. In this case
A) a person who Leolani interacted with (LeolaniFriends:X)
B) a person who Leolani heard about through someone else (LeolaniWorld:X)
Currently done through owl:sameAs, but it is not working/in use
The following folders are needed to run the main responder, but are not created on installation:
backups/brain
people/friends
Formal modelling of hyponomy as Python objects. These should work at the ontology level, including any learning the robot has acquired through conversation or linked data.
For expanding the ontology, we could include wordnet or the querying of it.
Create extra backend to work with a NAO robot. Robust against functionality available in Pepper but not for Nao (e.g tablet)
Improve text recognition by using several hypotheses
pepper/pepper/language/language.py
Line 418 in 233d39d
Identify speakers by their voice
This is a dog, not a cat: change object type and label to cat (give utterance, since we have context info there)
pepper/pepper/responder/sensory.py
Line 101 in 233d39d
This is piek’s office not armando’s office: change label of location in the current context. Like so:
pepper/pepper/responder/sensory.py
Line 211 in 233d39d
Since this project is becoming very big and complex, we would like to break it up into smaller services that can be managed in an easier way.
The idea is to break them down in a similar manner as
object recognition component calling services from a different repository:
pepper/pepper/framework/sensor/obj.py
Line 304 in 233d39d
face recognition component calling services from a docker:
pepper/pepper/framework/sensor/face.py
Line 58 in 233d39d
This method may be applied to other components like the brain, context, scene, etc
Copy of #10
Our current Data Model is entity-centric, but this must be changed to be event-centric.
For this we should use SEM
I am working with pepper for a simple task on windows platform and I see your work is very interesting. Would love to hear if this work is done on Windows platform. Would you be interested to have short email exchange ? Here is my email id [email protected]
I am currently working on some educational research exploration. So, I thought of having any guidance from you would be great head-start.
Thank you!!
Looking forward to hear from you soon.
See the specifications of the data to be stored per experiments:
Our current Data Model is entity-centric, but this must be changed to be event-centric.
For this we should use SEM
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