IROS Conference 2018 Conference Paper
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
- Fethiye Irmak Dogan
- Ilker Bozcan
- Mehmet Celik
- Sinan Kalkan
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.