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Semi-situated learning of verbal and nonverbal content for repeated human-robot interaction

Published: 31 October 2016 Publication History

Abstract

Content authoring of verbal and nonverbal behavior is a limiting factor when developing agents for repeated social interactions with the same user. We present PIP, an agent that crowdsources its own multimodal language behavior using a method we call semi-situated learning. PIP renders segments of its goal graph into brief stories that describe future situations, sends the stories to crowd workers who author and edit a single line of character dialog and its manner of expression, integrates the results into its goal state representation, and then uses the authored lines at similar moments in conversation. We present an initial case study in which the language needed to host a trivia game interaction is learned pre-deployment and tested in an autonomous system with 200 users "in the wild." The interaction data suggests that the method generates both meaningful content and variety of expression.

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Cited By

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  • (2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
  • (2024)Zero-Shot Learning to Enable Error Awareness in Data-Driven HRIProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634940(592-601)Online publication date: 11-Mar-2024
  • (2022)Correct Me If I'm Wrong: Using Non-Experts to Repair Reinforcement Learning PoliciesProceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction10.5555/3523760.3523825(493-501)Online publication date: 7-Mar-2022
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Published In

cover image ACM Conferences
ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction
October 2016
605 pages
ISBN:9781450345569
DOI:10.1145/2993148
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 31 October 2016

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Author Tags

  1. Long-term human-robot interaction
  2. content authoring
  3. crowdsourcing
  4. multimodal behavior generation

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

View all
  • (2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
  • (2024)Zero-Shot Learning to Enable Error Awareness in Data-Driven HRIProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634940(592-601)Online publication date: 11-Mar-2024
  • (2022)Correct Me If I'm Wrong: Using Non-Experts to Repair Reinforcement Learning PoliciesProceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction10.5555/3523760.3523825(493-501)Online publication date: 7-Mar-2022
  • (2022)Human Robot Collaboration for Enhancing Work ActivitiesHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720822107772266:1(158-179)Online publication date: 28-Mar-2022
  • (2022)Correct Me If I'm Wrong: Using Non-Experts to Repair Reinforcement Learning Policies2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI53351.2022.9889604(493-501)Online publication date: 7-Mar-2022
  • (2020)PatriccProceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3319502.3374792(399-407)Online publication date: 9-Mar-2020
  • (2019)Exploring Improvisational Approaches to Social Knowledge AcquisitionProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331804(1060-1068)Online publication date: 8-May-2019
  • (2019)Crowdsourcing a self-evolving dialog graphProceedings of the 1st International Conference on Conversational User Interfaces10.1145/3342775.3342790(1-8)Online publication date: 22-Aug-2019
  • (2019)Curiosity Did Not Kill the RobotACM Transactions on Human-Robot Interaction10.1145/33264628:3(1-24)Online publication date: 23-Jul-2019
  • (2019)Exploring Interaction with Remote Autonomous Systems using Conversational AgentsProceedings of the 2019 on Designing Interactive Systems Conference10.1145/3322276.3322318(1543-1556)Online publication date: 18-Jun-2019
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