pepper
pepper copied to clipboard
cltl
VU-CLTL Pepper/Nao Application Repository (Python 2)
Currently running on - [x] Windows 10 ("Pepper laptop"), local backend - [x] Windows 10 ("Pepper laptop"), pepper naoqi backend - [x] Mac OS, local backend - [x] Mac OS,...
See [the specifications](https://docs.google.com/spreadsheets/d/1TrRes-WEILkoEi_m1qJZ3n3pPk4ZbIdUKs6cqd9vVZ0/edit?usp=sharing) of the data to be stored per experiments: - [ ] Image https://cltl.github.io/pepper/build/html/pepper.framework.component.camera.html#pepper.framework.component.camera.CameraComponent.on_image - [ ] Audio https://cltl.github.io/pepper/build/html/pepper.framework.component.microphone.html#pepper.framework.component.microphone.MicrophoneComponent.on_audio - [ ] STT https://cltl.github.io/pepper/build/html/pepper.framework.component.speech_recognition.html#pepper.framework.component.speech_recognition.SpeechRecognitionComponent.on_transcript - [ ] Object...
Use pose recognition from: - https://github.com/tensorflow/tfjs-models/tree/master/posenet - https://github.com/deephdc/posenet-tf 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
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
FuzzyWuzzy is GPL Licensed, which is incompatible with the license used by this project. This Pull Request replaces FuzzyWuzzy with [RapidFuzz](https://github.com/maxbachmann/rapidfuzz), which implements the same algorithms. It is licensed in...
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...
Use scene classification from: https://github.com/IBM/MAX-Scene-Classifier Available as Docker
- [ ] Query for - [ ] - [ ] hunger for knowledge / exploration - [ ] broad: entity that I do not know much about - [...
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,...