Robotic Comfort Zones

Abstract

This paper investigates how the psychological notion of comfort can be useful in the design of robotic systems. A review of the existing study of human comfort, especially regarding its presence in infants, is conducted with the goal being to determine the relevant characteristics for mapping it onto the robotics domain. Focus is placed on the identification of the salient features in the environment that affect comfort level. Factors involved include current state familiarity, working conditions, the amount and location of available resources, etc. As part of The authors' newly developed comfort function theory, the notion of an object as a psychological attachment for a robot also is introduced, as espoused in Bowlby's theory of attachment. The output space of the comfort function and its dependency on the comfort level are analyzed. The results of the derivation of this comfort function are then presented in terms of the impact they have on robotic behavior. Justification for the use of the comfort function in the domain of robotics is presented with relevance for real-world operations. Also, a transformation of the theoretical discussion into a mathematical framework suitable for implementation within a behavior-based control system is presented. The paper concludes with results of simulation studies and real robot experiments using the derived comfort function.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA442294

Entities

People

  • Maxim Likhachev
  • Ronald C. Arkin

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Attachment
  • Autonomous Agents
  • Autonomous Systems
  • Body Temperature
  • Control Systems
  • Environment
  • Human Behavior
  • Intensity
  • Mathematical Models
  • Models
  • Psychology
  • Robotics
  • Robots
  • Simulations

Fields of Study

  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Materials Science
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Human-Robot Interaction
  • Space
  • Space - Spacecraft Maneuvers