Start Date
9-5-2019 3:30 PM
End Date
9-5-2019 5:00 PM
Document Type
Event
Keywords
Humanoid, framework, model, AI, Nao
Description
High quality probabilistic models for a complex robot like Alde- baran’s Nao humanoid depend heavily on details from its envi- ronment, involving multiple parameters. Building such models re- quires significant effort with data gathering and data cleaning. We propose to create a public database of NAO sensor data that can be used by researchers and engineers training models for localization, mapping, and planning in controlled environments. Here we report on our release of a database with sensor data, parameterized by en- vironment configuration and nature. The database contains struc- tured folders with documentation, measurements, Nao AI feedback, and data management tools. The measurements can include transi- tion, sonar, and image data while the internal AI feedback contains results of deFlorida Atlantic Universitylt landmark detection.
DOI
https://doi.org/10.5038/JGEH3147
Towards Sensor and Motion Measurements Databases for Training Models for the NAO Robot
High quality probabilistic models for a complex robot like Alde- baran’s Nao humanoid depend heavily on details from its envi- ronment, involving multiple parameters. Building such models re- quires significant effort with data gathering and data cleaning. We propose to create a public database of NAO sensor data that can be used by researchers and engineers training models for localization, mapping, and planning in controlled environments. Here we report on our release of a database with sensor data, parameterized by en- vironment configuration and nature. The database contains struc- tured folders with documentation, measurements, Nao AI feedback, and data management tools. The measurements can include transi- tion, sonar, and image data while the internal AI feedback contains results of deFlorida Atlantic Universitylt landmark detection.