CINet: A Learning Based Approach to Incremental Context Modeling in Robots

There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we so...

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Bibliographic Details
Main Authors: Irmak Dogan, Fethiye, Bozcan, Ilker, Celik, Mehmet, Kalkan, Sinan
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Context modeling
Recurrent neural networks
Resource management
Robots
Testing
Training
Online Access:Request full text
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Summary:There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we solve using a Recurrent Neural Network. We show that the network successfully (with 98% testing accuracy) learns to predict when to increment, and demonstrate, in a scene modeling problem (where the correct number of contexts is not known), that the robot increments the number of contexts in an expected manner (i.e., the entropy of the system is reduced). We also present how the incremental model can be used for various scene reasoning tasks.
ISSN:2153-0866
DOI:10.1109/IROS.2018.8593633