# Value of Assistance for Mobile Agents

arXiv: https://arxiv.org/abs/2308.11961 · HTML: https://mlsanity.com/paper/2308.11961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments](https://mlsanity.com/paper/2004.12317/) (2020)
- [Helpfulness as a Key Metric of Human-Robot Collaboration](https://mlsanity.com/paper/2010.04914/) (2020)
- [A Graph-Theoretic Analysis of Information Value](https://mlsanity.com/paper/1302.3596/) (2013)

## Built on by (most cited first, 1 of 1)

- [Value of Assistance for Grasping](https://mlsanity.com/paper/2310.14402/) (2023)

## Abstract (arXiv)

Mobile robotic agents often suffer from localization uncertainty which grows with time and with the agents' movement. This can hinder their ability to accomplish their task. In some settings, it may be possible to perform assistive actions that reduce uncertainty about a robot's location. For example, in a collaborative multi-robot system, a wheeled robot can request assistance from a drone that can fly to its estimated location and reveal its exact location on the map or accompany it to its intended location. Since assistance may be costly and limited, and may be requested by different members of a team, there is a need for principled ways to support the decision of which assistance to provide to an agent and when, as well as to decide which agent to help within a team. For this purpose, we propose Value of Assistance (VOA) to represent the expected cost reduction that assistance will yield at a given point of execution. We offer ways to compute VOA based on estimations of the robot's future uncertainty, modeled as a Gaussian process. We specify conditions under which our VOA measures are valid and empirically demonstrate the ability of our measures to predict the agent's average cost reduction when receiving assistance in both simulated and real-world robotic settings.