國立虎尾科技大學 |

Aligning Robot Representations With Humans.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Aligning Robot Representations With Humans./
Author:	Bobu, Andreea.
Description:	1 online resource (226 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9798380369060

Aligning Robot Representations With Humans.
Bobu, Andreea.

Aligning Robot Representations With Humans. - 1 online resource (226 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

Includes bibliographical references

Robots are becoming increasingly weaved into the fabric of our society, from self-driving cars on our streets to assistive manipulators in our homes. To act in the world, robots rely on a representation of salient features of the task: for example, to hand me a cup of coffee, the robot considers movement efficiency and cup orientation in its behavior. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. their representations must be aligned with humans'. What's holding us back from successful human-robot interaction is that these representations are often misaligned, resulting in anything from miscoordination and misunderstandings, to learning and executing dangerous behaviors.To learn the human's representation of what matters in a task, typical methods rely on data sets of human behavior but this data cannot reflect every individual, environment, and task the robot will be exposed to. This dissertation advocates that we should instead treat humans as active participants in the interaction not as static data sources: robots must engage with humans in an interactive process for finding a shared representation. We formalize the representation alignment problem as a joint search for a common representation. Then, rather than hoping that representations will naturally be aligned, we propose having humans directly teach them to robots with representation-specific input. Next, we enable robots to automatically detect representation misalignment with the human by estimating a confidence over how much the robot's representation can explain the human's behavior. We demonstrate how human-aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science. Finally, this thesis concludes by asking "How can robots help the human-robot team converge to a shared representation?" and discusses opportunities for future work in expanding representation alignment for seamless human-robot interaction.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380369060Subjects--Topical Terms:

561941
Robotics.
Subjects--Index Terms:

Salient featuresIndex Terms--Genre/Form:

554714
Electronic books.

Aligning Robot Representations With Humans.
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502 $a Thesis (Ph.D.)--University of California, Berkeley, 2023.
504 $a Includes bibliographical references
520 $a Robots are becoming increasingly weaved into the fabric of our society, from self-driving cars on our streets to assistive manipulators in our homes. To act in the world, robots rely on a representation of salient features of the task: for example, to hand me a cup of coffee, the robot considers movement efficiency and cup orientation in its behavior. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. their representations must be aligned with humans'. What's holding us back from successful human-robot interaction is that these representations are often misaligned, resulting in anything from miscoordination and misunderstandings, to learning and executing dangerous behaviors.To learn the human's representation of what matters in a task, typical methods rely on data sets of human behavior but this data cannot reflect every individual, environment, and task the robot will be exposed to. This dissertation advocates that we should instead treat humans as active participants in the interaction not as static data sources: robots must engage with humans in an interactive process for finding a shared representation. We formalize the representation alignment problem as a joint search for a common representation. Then, rather than hoping that representations will naturally be aligned, we propose having humans directly teach them to robots with representation-specific input. Next, we enable robots to automatically detect representation misalignment with the human by estimating a confidence over how much the robot's representation can explain the human's behavior. We demonstrate how human-aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science. Finally, this thesis concludes by asking "How can robots help the human-robot team converge to a shared representation?" and discusses opportunities for future work in expanding representation alignment for seamless human-robot interaction.
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