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Grounded Word Learning on a Pepper Robot

Published: 05 November 2018 Publication History

Abstract

In this demonstration, we will showcase realtime grounded language learning on the humanoid robot Pepper. In particular, learning word-object and word-action mapping from cross-modal data, where simple actions, such as take, put and push, are shown to the robot by a human tutor. The visual demonstration is accompanied by verbal descriptions of the performed actions, such as I take the box and put it next to the bottle. Learning was realized on the humanoid robot Pepper using the Google Speech API for speech to text and the robot's camera system for object tracking.

References

[1]
Amir Aly and Tadahiro Taniguchi. 2018. Towards Understanding Object-Directed Actions: A Generative Model for Grounding Syntactic Categories of Speech through Visual Perception. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA) 2018. IEEE.
[2]
Johann Prankl, Aitor Aldoma, Alexander Svejda, and Markus Vincze. 2015. RGB-D object modelling for object recognition and tracking. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE, 96--103.
[3]
Jun Tani, Masato Ito, and Yuuya Sugita. 2004. Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks 17, 8-9 (2004), 1273--1289.

Cited By

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  • (2023)Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive reviewElectronic Research Archive10.3934/era.202333431:11(6600-6633)Online publication date: 2023
  • (2021)Waiting for Tactile: Robotic and Virtual Experiences in the FogACM Transactions on Internet Technology10.1145/342150721:3(1-19)Online publication date: 15-Jun-2021
  • (2021)Investigating Transparency Methods in a Robot Word-Learning System and Their Effects on Human Teaching Behaviors2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)10.1109/RO-MAN50785.2021.9515518(175-182)Online publication date: 8-Aug-2021
  • Show More Cited By

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Published In

cover image ACM Conferences
IVA '18: Proceedings of the 18th International Conference on Intelligent Virtual Agents
November 2018
381 pages
ISBN:9781450360135
DOI:10.1145/3267851
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2018

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Author Tags

  1. Grounded language learning
  2. incremental cross-situational and cross-modal learning
  3. robot learning

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  • Abstract
  • Research
  • Refereed limited

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IVA '18
Sponsor:
IVA '18: International Conference on Intelligent Virtual Agents
November 5 - 8, 2018
NSW, Sydney, Australia

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IVA '18 Paper Acceptance Rate 17 of 82 submissions, 21%;
Overall Acceptance Rate 53 of 196 submissions, 27%

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Cited By

View all
  • (2023)Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive reviewElectronic Research Archive10.3934/era.202333431:11(6600-6633)Online publication date: 2023
  • (2021)Waiting for Tactile: Robotic and Virtual Experiences in the FogACM Transactions on Internet Technology10.1145/342150721:3(1-19)Online publication date: 15-Jun-2021
  • (2021)Investigating Transparency Methods in a Robot Word-Learning System and Their Effects on Human Teaching Behaviors2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)10.1109/RO-MAN50785.2021.9515518(175-182)Online publication date: 8-Aug-2021
  • (2020)Models of Cross-Situational and Crossmodal Word Learning in Task-Oriented ScenariosIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2020.299504512:3(658-668)Online publication date: Sep-2020

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