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Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot\u2019s internal state to the human (i.e., transferring information from robots to humans). Prior research often\n            <jats:italic>separates<\/jats:italic>\n            these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that\n            <jats:italic>close the loop<\/jats:italic>\n            between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot\u2019s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that\u2014when learning and communication are developed together\u2014the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior.\n          <\/jats:p>","DOI":"10.1177\/02783649241281369","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T12:39:42Z","timestamp":1728391182000},"page":"665-698","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["A survey of communicating robot learning during human-robot interaction"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4103-4664","authenticated-orcid":false,"given":"Soheil","family":"Habibian","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1810-8234","authenticated-orcid":false,"given":"Antonio","family":"Alvarez Valdivia","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0658-5364","authenticated-orcid":false,"given":"Laura H.","family":"Blumenschein","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8787-5293","authenticated-orcid":false,"given":"Dylan P.","family":"Losey","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA"}]}],"member":"179","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132743"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TOH.2023.3240400"},{"key":"e_1_3_4_4_1","first-page":"1168","volume-title":"International Conference on Autonomous Agents and MultiAgent Systems","author":"Amir D","year":"2018","unstructured":"Amir D, Amir O (2018) Highlights: summarizing agent behavior to people. 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