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Safe and Efficient Estimation for Robotics through the Optimal Use of Resources

Frederike Dümbgen

TL;DR

The paper addresses robust state and future-state estimation for robots in uncertain, real-world environments. It combines three pillars—multi-modality sensing (including RF and audio), certifiably optimal solvers with semidefinite relaxations and duality certificates, and flexible, learnable models via automated problem formulation and Koopman lifting, potentially embedded in differentiable pipelines. Key contributions include demonstrations of RF- and audio-assisted localization, extension of global optimality techniques to diverse localization/SLAM problems, and methods for automatic, data-driven model learning that remain solvable and transparent. The envisioned impact is a resource-efficient, resilient estimation framework with broad applicability beyond robotics, enabling robust operation under low SNR and non-ideal sensing conditions.

Abstract

In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and build models and algorithms that, given some measurements, produce estimates of the current or future states. Environments can be unpredictable and sensors are not perfect. Therefore, it is important to both use all information available, and to do so optimally: making sure that we get the best possible answer from the amount of information we have. However, in prevalent research, uncommon sensors, such as sound or radio-frequency signals, are commonly ignored for state estimation; and the most popular solvers employed to produce state estimates are only of local nature, meaning they may produce suboptimal estimates for the typically non-convex estimation problems. My research aims to use resources more optimally, by building on 1) multi-modality: using ubiquitous RF transceivers and microphones to support state estimation, 2) building certifiably optimal solvers and 3) learning and improving adequate models from data.

Safe and Efficient Estimation for Robotics through the Optimal Use of Resources

TL;DR

The paper addresses robust state and future-state estimation for robots in uncertain, real-world environments. It combines three pillars—multi-modality sensing (including RF and audio), certifiably optimal solvers with semidefinite relaxations and duality certificates, and flexible, learnable models via automated problem formulation and Koopman lifting, potentially embedded in differentiable pipelines. Key contributions include demonstrations of RF- and audio-assisted localization, extension of global optimality techniques to diverse localization/SLAM problems, and methods for automatic, data-driven model learning that remain solvable and transparent. The envisioned impact is a resource-efficient, resilient estimation framework with broad applicability beyond robotics, enabling robust operation under low SNR and non-ideal sensing conditions.

Abstract

In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and build models and algorithms that, given some measurements, produce estimates of the current or future states. Environments can be unpredictable and sensors are not perfect. Therefore, it is important to both use all information available, and to do so optimally: making sure that we get the best possible answer from the amount of information we have. However, in prevalent research, uncommon sensors, such as sound or radio-frequency signals, are commonly ignored for state estimation; and the most popular solvers employed to produce state estimates are only of local nature, meaning they may produce suboptimal estimates for the typically non-convex estimation problems. My research aims to use resources more optimally, by building on 1) multi-modality: using ubiquitous RF transceivers and microphones to support state estimation, 2) building certifiably optimal solvers and 3) learning and improving adequate models from data.
Paper Structure (6 sections, 2 figures)

This paper contains 6 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Past research
  3. Multi-modality
  4. Optimality
  5. Flexibility
  6. Ongoing and future research

Figures (2)

  • Figure 1: Three research directions to create more resilient robots by optimally using resources: using all available sensors (multi-modality), using optimal solvers (optimality), and learning models that can be adapted and solved efficiently (flexibility).
  • Figure 2: Overview of the used pipelines for modeling and solving for higher optimality and flexibility. The three main directions for future research are also depicted.