Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction-Reference-Cited by-同舟云学术

Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction

Published:2023-03-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
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Author:

Zhang Qiping¹^ORCID,Narcomey Austin¹^ORCID,Candon Kate¹^ORCID,Vázquez Marynel¹^ORCID

Affiliation:

1. Yale University, New Haven, CT, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3568162.3576986

Reference79 articles.

1. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

2. Neziha Akalin and Amy Loutfi . 2021. Reinforcement Learning Approaches in Social Robotics. Sensors 21, 4 ( 2021 ). https://doi.org/10.3390/s21041292 10.3390/s21041292 Neziha Akalin and Amy Loutfi. 2021. Reinforcement Learning Approaches in Social Robotics. Sensors 21, 4 (2021). https://doi.org/10.3390/s21041292

3. Riku Arakawa Sosuke Kobayashi Yuya Unno Yuta Tsuboi and Shin-ichi Maeda. 2018. DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback. https://doi.org/10.48550/ARXIV.1810.11748 10.48550/ARXIV.1810.11748

4. Riku Arakawa Sosuke Kobayashi Yuya Unno Yuta Tsuboi and Shin-ichi Maeda. 2018. DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback. https://doi.org/10.48550/ARXIV.1810.11748

5. A survey of robot learning from demonstration

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