Challenges with incorporating context into human-robot teaming

Kristin E. Schaefer, Jessie Y.C. Chen, Julia Wright, Derya Aksaray, Nicholas Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Incorporating contextual cues and contextual understanding into the development of an autonomous robot can advance the robot's decision-making capabilities, but may also help improve the human's understanding of those decisions to improve team effectiveness. While a number of these benefits are discussed below, there are just as many, if not more, challenges associated with developing context-driven autonomy. These benefits and challenges are discussed below to support ongoing and future human-robot teaming efforts.

Original languageEnglish (US)
Title of host publicationSS-17-01
Subtitle of host publicationArtificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing
PublisherAI Access Foundation
Pages347-350
Number of pages4
ISBN (Electronic)9781577357797
StatePublished - Jan 1 2017
Externally publishedYes
Event2017 AAAI Spring Symposium - Stanford, United States
Duration: Mar 27 2017Mar 29 2017

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-17-01 - SS-17-08

Other

Other2017 AAAI Spring Symposium
CountryUnited States
CityStanford
Period3/27/173/29/17

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