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 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.

Authors

• Fethiye Irmak Dogan
• Ilker Bozcan
• Mehmet Celik
• Sinan Kalkan

Keywords

• Context modeling
• Training
• Robots
• Computational modeling
• Resource management
• Recurrent neural networks
• Testing
• Incremental Model
• Recurrent Neural Network
• Learning Problem
• Entropy Of The System
• Number Of Contexts
• Scene Model
• Computer Vision
• Object Detection
• Type Of Environment
• Network Input
• Topic Modeling
• Markov Random Field
• Objects In The Scene
• Latent Dirichlet Allocation
• Artificial Data
• Artificial Datasets
• Latent Dirichlet Allocation Model