Abstract
With robots poised to enter our daily environments, they will not only need to work for people, but also learn from them. An active area of investigation in the robotics, machine learning, and human-robot interaction communities is the design of teachable robots that can learn interactively from humans. To refer to these research efforts, we use the umbrella term Human-Interactive Robot Learning (HIRL). In the last two years, we began consolidating what defines HIRL in terms of long, medium, and short-term research problems and what the different communities can contribute to those problems. With this third installment of the HIRL workshop, we aim at further consolidating this community and, specifically this year, discuss how the recent widespread of Large Language Models (LLMs) will impact the teaching of robots and explore the opportunities and challenges presented by robots' nature of embodied agents.
Original language | English |
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Title of host publication | HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction |
Publisher | IEEE Computer Society |
Pages | 1349-1351 |
Number of pages | 3 |
ISBN (Electronic) | 9798400703232 |
DOIs | |
State | Published - 11 Mar 2024 |
Event | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - Boulder, United States Duration: 11 Mar 2024 → 15 Mar 2024 |
Publication series
Name | ACM/IEEE International Conference on Human-Robot Interaction |
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ISSN (Electronic) | 2167-2148 |
Conference
Conference | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 |
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Country/Territory | United States |
City | Boulder |
Period | 11/03/24 → 15/03/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s)
Keywords
- Interactive robot learning
- Learning from human input
- Socially intelligent robots
- Socially interactive learning