Challenges with incorporating context into human-robot teaming

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

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	SS-17-01
Subtitle of host publication	Artificial 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
Publisher	AI Access Foundation
Pages	347-350
Number of pages	4
ISBN (Electronic)	9781577357797
State	Published - Jan 1 2017
Externally published	Yes
Event	2017 AAAI Spring Symposium - Stanford, United States Duration: Mar 27 2017 → Mar 29 2017

Publication series

Name	AAAI Spring Symposium - Technical Report
Volume	SS-17-01 - SS-17-08

Other

Other	2017 AAAI Spring Symposium
Country/Territory	United States
City	Stanford
Period	3/27/17 → 3/29/17

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Schaefer, K. E., Chen, J. Y. C., Wright, J., Aksaray, D., & Roy, N. (2017). Challenges with incorporating context into human-robot teaming. In SS-17-01: Artificial 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 (pp. 347-350). (AAAI Spring Symposium - Technical Report; Vol. SS-17-01 - SS-17-08). AI Access Foundation.

Challenges with incorporating context into human-robot teaming. / Schaefer, Kristin E.; Chen, Jessie Y.C.; Wright, Julia et al.
SS-17-01: Artificial 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. AI Access Foundation, 2017. p. 347-350 (AAAI Spring Symposium - Technical Report; Vol. SS-17-01 - SS-17-08).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Schaefer, KE, Chen, JYC, Wright, J, Aksaray, D & Roy, N 2017, Challenges with incorporating context into human-robot teaming. in SS-17-01: Artificial 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. AAAI Spring Symposium - Technical Report, vol. SS-17-01 - SS-17-08, AI Access Foundation, pp. 347-350, 2017 AAAI Spring Symposium, Stanford, United States, 3/27/17.

Schaefer KE, Chen JYC, Wright J, Aksaray D, Roy N. Challenges with incorporating context into human-robot teaming. In SS-17-01: Artificial 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. AI Access Foundation. 2017. p. 347-350. (AAAI Spring Symposium - Technical Report).

Schaefer, Kristin E. ; Chen, Jessie Y.C. ; Wright, Julia et al. / Challenges with incorporating context into human-robot teaming. SS-17-01: Artificial 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. AI Access Foundation, 2017. pp. 347-350 (AAAI Spring Symposium - Technical Report).

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Challenges with incorporating context into human-robot teaming

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