CINet: A Learning Based Approach to Incremental Context Modeling in Robots

TL;DR

This paper introduces CINet, a learning-based method using RNNs to determine when robots should increment their context models, achieving high accuracy and improving scene understanding.

Contribution

The paper presents a novel RNN-based approach for learning when to increment context models in robots, moving beyond fixed or rule-based methods.

Findings

Achieved 98% accuracy in predicting context increments.

Successfully reduced system entropy during scene modeling.

Enabled improved scene reasoning with incremental context models.

Abstract

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.