A Learning Based Approach to Incremental Context Modeling in Robots
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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 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.
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