A Scavenger Hunt for Service Robots

Harel Yedidsion, Jennifer Suriadinata, Zifan Xu, Stefan Debruyn, Peter Stone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Creating robots that can perform general-purpose service tasks in a human-populated environment has been a longstanding grand challenge for AI and Robotics research. One particularly valuable skill that is relevant to a wide variety of tasks is the ability to locate and retrieve objects upon request. This paper models this skill as a Scavenger Hunt (SH) game, which we formulate as a variation of the NP-hard stochastic traveling purchaser problem. In this problem, the goal is to find a set of objects as quickly as possible, given probability distributions of where they may be found. We investigate the performance of several solution algorithms for the SH problem, both in simulation and on a real mobile robot. We use Reinforcement Learning (RL) to train an agent to plan a minimal cost path, and show that the RL agent can outperform a range of heuristic algorithms, achieving near optimal performance. In order to stimulate research on this problem, we introduce a publicly available software stack and associated website that enable users to upload scavenger hunts which robots can download, perform, and learn from to continually improve their performance on future hunts.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7774-7780
Number of pages7
ISBN (Electronic)9781728190778
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: 30 May 20215 Jun 2021

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN (Print)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period30/05/215/06/21

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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