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This paper amalgamates recent human factors insights and computational techniques that can support human-robot alignment through interactive and interpretable robot learning and teaming.","doi":"10.24963/ijcai.2024/976","url":"https://ijcai.org/proceedings/2024/976","pdf":"https://ijcai.org/proceedings/2024/0976.pdf","markdown":"https://ijcai.org/proceedings/2024/976.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/976","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Early Career","track":"Early Career","number":976,"pages":{"first":8523,"last":8528},"keywords":["Robotics: ROB: Human robot interaction","Robotics: ROB: Learning in robotics","Machine Learning: ML: Explainable/Interpretable machine learning"],"videos":[],"references":[{"position":1,"raw":"[Altundas et al., 2022] Batuhan Altundas, Zheyuan Wang, Joshua Bishop, and Matthew Gombolay. 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