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We utilize tests accordingly to close the loop and maintain a novel particle filter model of human beliefs throughout the learning process, allowing us to provide demonstrations that are targeted at the human\u2019s current understanding in real time. A user study finds that our proposed closed-loop teaching framework reduces the regret (i.e., the suboptimality) of human test responses by 43% over an open-loop baseline. We also compare our closed-loop teaching framework against another baseline of directly communicating the robot\u2019s reward function in a second user study. We find that our closed-loop teaching outperforms direct reward communication by 64%, but we also observe synergies from the use of both teaching forms. Finally, we observe strong interaction effects between the teaching form and the domains considered in both user studies, seeing increased learning outcomes from well-designed demonstration-based teaching in the more challenging domain.<\/jats:p>","DOI":"10.1145\/3743150","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T12:25:36Z","timestamp":1749558336000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving the Transparency of Robot Policies Using Demonstrations and Reward Communication"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8659-3041","authenticated-orcid":false,"given":"Michael S.","family":"Lee","sequence":"first","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3153-0453","authenticated-orcid":false,"given":"Reid","family":"Simmons","sequence":"additional","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1796-2196","authenticated-orcid":false,"given":"Henny","family":"Admoni","sequence":"additional","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015430"},{"key":"e_1_3_2_3_2","first-page":"287","volume-title":"Conference on Robot Learning","author":"Ahn Michael","year":"2023","unstructured":"Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2023. 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