CINet: A Learning Based Approach to Incremental Context Modeling in Robots
This addresses incremental context modeling for robots, offering a learning-based alternative to rule-based methods, though it is incremental in approach.
The paper tackles the problem of when to increment contexts in robot modeling by framing it as a learning task using a Recurrent Neural Network, achieving 98% testing accuracy in predictions and reducing system entropy in scene modeling.
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.