Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning-Reference-Cited by-同舟云学术

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Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning

Published:2024-03-11 Issue: Volume:11 Page:1323-1325
ISSN:
Container-title:Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
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Author:

Irfan Bahar¹^ORCID,Staffa Mariacarla²^ORCID,Bobu Andreea³^ORCID,Churamani Nikhil⁴^ORCID

Affiliation:

1. Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Stockholm, Sweden

2. University of Naples Parthenope, Naples, Italy

3. Boston Dynamics AI Institute, Cambridge, MA, USA

4. University of Cambridge, Cambridge, United Kingdom

Funder

Google

KTH Digital Futures

Italian Ministry of University and Research

Next Generation EU

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610978.3638159

Reference19 articles.

1. David Abel, Andre Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, and Satinder Singh. 2023. A Definition of Continual Reinforcement Learning. In Thirty-seventh Conference on Neural Information Processing Systems.

2. Abhijit Bendale and Terrance Boult. 2015. Towards Open World Recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.

3. Diana Borsa, Thore Graepel, and John Shawe-Taylor. 2016. Learning shared representations in multi-task reinforcement learning. arXiv preprint:1603.02041 (2016).

4. Zhiyuan Chen and Bing Liu. 2018. Lifelong Machine Learning. Springer International Publishing, Cham.

5. Continual Learning for Affective Robotics: Why, What and How?

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