Comparison of Information-Gain Criteria for Action Selection

TL;DR

This paper evaluates different information gain criteria for action selection in robotic object pose estimation, demonstrating the effectiveness of a novel Bayesian filter-based approach across various criteria and measurement densities.

Contribution

It introduces the TIQF method for pose estimation and empirically compares multiple information gain criteria for action selection in robotics.

Findings

Similar pose accuracy with sparse measurements across criteria

Effectiveness of TIQF in diverse information gain scenarios

Exploration of uncommon information theoretic criteria in robotics

Abstract

Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) is proposed for pose estimation using point cloud registration. Active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain as tactile data collection is time consuming. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We find similar performance in terms of pose accuracy with sparse measurements (<15 points) across all the selected criteria. Furthermore, we explore the use of uncommon information theoretic criteria in the robotics domain for action selection.