S. Arif, S.A. Khan, and L. Bölöni

Balancing predicted mission cost and social costs by mobile robots navigating a crowd


Cite as:

S. Arif, S.A. Khan, and L. Bölöni. Balancing predicted mission cost and social costs by mobile robots navigating a crowd. In Proc. of Autonomous Robots and Multirobot Systems (ARMS) workshop at AAMAS-2014, May 2014.

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Abstract:

Recent developments in mobile robotics made feasible the near future scenario of mobile robots assisting individual persons. Such robots must maintain a sufficient distance from their human owners to be able to offer assistance, but they also need to be inconspicuous and observe the prevailing social and cultural norms. We are considering a scenario of mobile robots assisting a peacekeeper soldier patrolling a market with a dense crowd. The robot must balance the costs related to its mission (the danger of loosing contact with its owner) and the social cost of violating the crowd members' personal space. We develop a technique through which we predict the mission cost of different decisions, and use it to adapt the robot's strategies for resolving the micro-conflicts encountered in crowd navigation. We show that this adaptive strategy outperforms strategies of consistent politeness / assertiveness over a variety of scenarios.

BibTeX:

@inproceedings{Arif-2014-ARMS,
title = "Balancing predicted mission cost and social costs by mobile robots navigating a crowd",
author = "S. Arif and S.A. Khan and L. B{\"o}l{\"o}ni",
booktitle = "Proc. of Autonomous Robots and Multirobot Systems (ARMS) workshop at AAMAS-2014",
year = "2014",
month = "May",
abstract = {
  Recent developments in mobile robotics made feasible the near future
  scenario of mobile robots assisting individual persons. Such robots
  must maintain a sufficient distance from their human owners to be
  able to offer assistance, but they also need to be inconspicuous and
  observe the prevailing social and cultural norms. We are considering
  a scenario of mobile robots assisting a peacekeeper soldier
  patrolling a market with a dense crowd. The robot must balance the
  costs related to its mission (the danger of loosing contact with its
  owner) and the social cost of violating the crowd members' personal
  space. We develop a technique through which we predict the mission
  cost of different decisions, and use it to adapt the robot's
  strategies for resolving the micro-conflicts encountered in crowd
  navigation. We show that this adaptive strategy outperforms
  strategies of consistent politeness / assertiveness over a variety of
  scenarios.
 },
}

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