Article

Integrated learning for interactive synthetic characters

Authors:

Bruce Blumberg,

Michael Patrick Johnson,

Bill TomlinsonAuthors Info & Claims

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

Pages 417 - 426

https://doi.org/10.1145/566570.566597

Published: 01 July 2002 Publication History

Abstract

The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning for synthetic characters. Our implementation is grounded in the techniques of reinforcement learning and informed by insights from animal training. It simplifies the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans.We built an autonomous animated dog that can be trained with a technique used to train real dogs called "clicker training". Capabilities demonstrated include being trained to recognize and use acoustic patterns as cues for actions, as well as to synthesize new actions from novel paths through its motion space.A key contribution of this paper is to demonstrate that by addressing the three problems of state, action, and state-action space discovery at the same time, the solution for each becomes easier. Finally, we articulate heuristics and design principles that make learning practical for synthetic characters.

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Cited By

Butt NAwais MShahzadi SKim TAshraf I(2024)Towards the development of believable agents: Adopting neural architectures and adaptive neuro-fuzzy inference system via playback of human tracesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10218236:8(102182)Online publication date: Oct-2024
https://doi.org/10.1016/j.jksuci.2024.102182
Trick SHerbert FRothkopf CKoert D(2022)Interactive Reinforcement Learning With Bayesian Fusion of Multimodal AdviceIEEE Robotics and Automation Letters10.1109/LRA.2022.31821007:3(7558-7565)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3182100
Chisari EWelschehold TBoedecker JBurgard WValada A(2022)Correct Me If I am Wrong: Interactive Learning for Robotic ManipulationIEEE Robotics and Automation Letters10.1109/LRA.2022.31455167:2(3695-3702)Online publication date: Apr-2022
https://doi.org/10.1109/LRA.2022.3145516
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Index Terms

Integrated learning for interactive synthetic characters
1. Computing methodologies
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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cover image ACM Conferences

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

July 2002

574 pages

ISBN:1581135211

DOI:10.1145/566570

Conference Chair:
Tom Appolloni
Harris Corporation

Copyright © 2002 ACM.

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 ACM 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: 01 July 2002

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SIGGRAPH02: The 29th International Conference on Computer Graphics and Interactive Techniques

July 23 - 26, 2002

Texas, San Antonio

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SIGGRAPH '02 Paper Acceptance Rate 67 of 358 submissions, 19%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

Butt NAwais MShahzadi SKim TAshraf I(2024)Towards the development of believable agents: Adopting neural architectures and adaptive neuro-fuzzy inference system via playback of human tracesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10218236:8(102182)Online publication date: Oct-2024
https://doi.org/10.1016/j.jksuci.2024.102182
Trick SHerbert FRothkopf CKoert D(2022)Interactive Reinforcement Learning With Bayesian Fusion of Multimodal AdviceIEEE Robotics and Automation Letters10.1109/LRA.2022.31821007:3(7558-7565)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3182100
Chisari EWelschehold TBoedecker JBurgard WValada A(2022)Correct Me If I am Wrong: Interactive Learning for Robotic ManipulationIEEE Robotics and Automation Letters10.1109/LRA.2022.31455167:2(3695-3702)Online publication date: Apr-2022
https://doi.org/10.1109/LRA.2022.3145516
Lin JMa ZGomez RNakamura KHe BLi G(2020)A Review on Interactive Reinforcement Learning From Human Social FeedbackIEEE Access10.1109/ACCESS.2020.30062548(120757-120765)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3006254
Li GWhiteson SKnox WHung H(2018)Social interaction for efficient agent learning from human rewardAutonomous Agents and Multi-Agent Systems10.1007/s10458-017-9374-832:1(1-25)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10458-017-9374-8
Phillips ESchaefer KBillings DJentsch FHancock P(2016)Human-animal teams as an analog for future human-robot teamsJournal of Human-Robot Interaction10.5898/JHRI.5.1.Phillips5:1(100-125)Online publication date: 23-Mar-2016
https://dl.acm.org/doi/10.5898/JHRI.5.1.Phillips
Chao CThomaz A(2016)Timed Petri nets for fluent turn-taking over multimodal interaction resources in human-robot collaborationInternational Journal of Robotics Research10.1177/027836491562729135:11(1330-1353)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1177/0278364915627291
Li GWhiteson SKnox WHung H(2016)Using informative behavior to increase engagement while learning from human rewardAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9308-230:5(826-848)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10458-015-9308-2
Harris TGittens C(2015)Modeling believable agents using a descriptive approachBiologically Inspired Cognitive Architectures10.1016/j.bica.2015.09.00414(10-21)Online publication date: Oct-2015
https://doi.org/10.1016/j.bica.2015.09.004
Yanik PManganelli JMerino JThreatt ABrooks JGreen KWalker I(2014)A Gesture Learning Interface for Simulated Robot Path Shaping With a Human TeacherIEEE Transactions on Human-Machine Systems10.1109/TSMC.2013.229171444:1(41-54)Online publication date: Feb-2014
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