Efficient Touch Based Localization through Submodularity

TL;DR

This paper introduces adaptive submodularity-based methods for efficient online touch-based localization in robotics, providing theoretical guarantees and computational speedups for sequential information gathering tasks.

Contribution

It develops new adaptive submodularity techniques for online action selection, improving efficiency and guarantees in robotic localization tasks.

Findings

Methods outperform baseline in simulation

Achieve faster localization with fewer actions

Effective on real robotic system

Abstract

Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well. Our work first explains this high performance -- we note a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. However, reacting online to observations can increase performance. Recently developed notions of adaptive submodularity provide guarantees for a greedy algorithm in this online setting. In this work, we develop new methods based on adaptive submodularity for selecting a sequence of information gathering actions online. In addition to providing guarantees, we can capitalize on submodularity to attain additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.