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 propose to pose the task of incrementing 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.
* Equal contribution
CINet: A Learning Based Approach to Incremental Context Modeling in Robots (IROS 2018)
In order to obtain dataset, we used Latent Dirichlet Allocation (LDA), which, being a generative model, allows one to sample artificial data with various number of contexts (topics). Therefore our dataset consist of scenes generated with k number of contexts. Then, we trained LDA models with k0 contexts s.t. k0 ≤ k. Finally, we used probabilities of contexts given objects obtained from LDA in order to train LSTM model.
Experiments and Results
Artificially Generated Dataset: