{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:28:21Z","timestamp":1767637701990,"version":"3.48.0"},"reference-count":36,"publisher":"Maximum Academic Press","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"unspecified","delay-in-days":315,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In order to enable robots to interact with humans in a natural way, they need to be able to autonomously learn new tasks. The most natural way for humans to tell another agent, which can be a human or robot, to perform a task is via natural language. Thus, natural human\u2013robot interactions also require robots to understand natural language, i.e. extract the meaning of words and phrases. To do this, words and phrases need to be linked to their corresponding percepts through grounding. Afterward, agents can learn the optimal micro-action patterns to reach the goal states of the desired tasks. Most previous studies investigated only learning of actions or grounding of words, but not both. Additionally, they often used only a small set of tasks as well as very short and unnaturally simplified utterances. In this paper, we introduce a framework that uses reinforcement learning to learn actions for several tasks and cross-situational learning to ground actions, object shapes and colors, and prepositions. The proposed framework is evaluated through a simulated interaction experiment between a human tutor and a robot. The results show that the employed framework can be used for both action learning and grounding.<\/jats:p>","DOI":"10.1017\/s0269888919000079","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T04:59:42Z","timestamp":1573534782000},"source":"Crossref","is-referenced-by-count":4,"title":["Action learning and grounding in simulated human\u2013robot interactions"],"prefix":"10.48130","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1852-7102","authenticated-orcid":false,"given":"Oliver","family":"Roesler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann","family":"Now\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"27968","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"S0269888919000079_ref36","doi-asserted-by":"publisher","DOI":"10.1111\/j.1551-6709.2011.1226.x"},{"key":"S0269888919000079_ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v32i4.2384"},{"key":"S0269888919000079_ref31","doi-asserted-by":"publisher","DOI":"10.1075\/ais.3.04ste"},{"key":"S0269888919000079_ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2007.06.010"},{"key":"S0269888919000079_ref28","first-page":"864","volume-title":"Cross-Situational Learning","author":"Smith","year":"2012"},{"key":"S0269888919000079_ref26","unstructured":"She, L. , Yang, S. , Cheng, Y. , Jia, Y. , Chai, J. Y. & Xi, N. 2014. Back to the blocks world: learning new actions through situated human-robot dialogue. In Proceedings of the SIGDIAL 2014 Conference, Philadelphia, USA, June 2014, 89\u201397."},{"key":"S0269888919000079_ref25","unstructured":"Rusu, R. B. , Bradski, G. , Thibaux, R. & Hsu, J. 2010. Fast 3D recognition and pose using the viewpoint feature histogram. In Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, October 2010, 2155\u20132162."},{"key":"S0269888919000079_ref23","unstructured":"Roesler, O. , Aly, A. , Taniguchi, T. & Hayashi, Y. 2018. A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning. In ICRA \u201918 Workshop on Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding, Brisbane, Australia, May 2018."},{"key":"S0269888919000079_ref9","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3841(94)90346-8"},{"key":"S0269888919000079_ref2","doi-asserted-by":"publisher","DOI":"10.1177\/014272379901905703"},{"key":"S0269888919000079_ref8","unstructured":"Dawson, C. R. , Wright, J. , Rebguns, A. , Esc\u00e1rcega, M. V. , Fried, D. & Cohen, P. R. 2013. A generative probabilistic framework for learning spatial language. In IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), Osaka, Japan, August 2013."},{"key":"S0269888919000079_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907006"},{"key":"S0269888919000079_ref3","unstructured":"Aly, A. , Taniguchi, A. & Taniguchi, T. 2017. A generative framework for multimodal learning of spatial concepts and object categories: an unsupervised part-of-speech tagging and 3D visual perception based approach. In IEEE International Conference on Development and Learning and the International Conference on Epigenetic Robotics (ICDL-EpiRob), Lisbon, Portugal, September 2017."},{"key":"S0269888919000079_ref34","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2017.00066"},{"key":"S0269888919000079_ref12","unstructured":"Fontanari, J. F. , Tikhanoff, V. , Cangelosi, A. & Perlovsky, L. I. 2009b. A cross-situational algorithm for learning a lexicon using neural modeling fields. In International Joint Conference on Neural Networks (IJCNN), Atlanta, GA, USA, June 2009."},{"key":"S0269888919000079_ref29","doi-asserted-by":"publisher","DOI":"10.1111\/j.1551-6709.2010.01158.x"},{"key":"S0269888919000079_ref24","unstructured":"Roesler, O. , Aly, A. , Taniguchi, T. & Hayashi, Y. 2019. Evaluation of word representations in grounding natural language instructions through computational human\u2013robot interaction. In Proceedings of the 14th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, South Korea, March 2019."},{"key":"S0269888919000079_ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2006.10.005"},{"key":"S0269888919000079_ref6","first-page":"265","volume-title":"Linguistic Theory and Psychological Reality","author":"Carey","year":"1978"},{"key":"S0269888919000079_ref5","doi-asserted-by":"publisher","DOI":"10.1111\/j.1551-6709.2009.01089.x"},{"key":"S0269888919000079_ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2008.10.024"},{"key":"S0269888919000079_ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.06.010"},{"key":"S0269888919000079_ref7","first-page":"17","article-title":"Acquiring a single new word","volume":"15","author":"Carey","year":"1978","journal-title":"Papers and Reports on Child Language Development"},{"key":"S0269888919000079_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-0277(99)00036-0"},{"key":"S0269888919000079_ref14","unstructured":"Gu, S. , Holly, E. , Lillicrap, T. & Levine, S. 2017. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In IEEE International Conference on Robotics and Automation (ICRA), Singapore, May\u2013June 2017."},{"key":"S0269888919000079_ref15","unstructured":"Gudimella, A. , Story, R. , Shaker, M. , Kong, R. , Brown, M. , Shnayder, V. & Campos, M. 2017. Deep reinforcement learning for dexterous manipulation with concept networks. CoRR. https:\/\/arxiv.org\/abs\/1709.06977."},{"volume-title":"Reinforcement Learning: An Introduction","year":"1998","author":"Sutton","key":"S0269888919000079_ref33"},{"key":"S0269888919000079_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(90)90087-6"},{"key":"S0269888919000079_ref17","unstructured":"International Federation of Robotics. 2017. World robotics 2017 - service robots."},{"key":"S0269888919000079_ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2012.2210294"},{"key":"S0269888919000079_ref27","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-0277(96)00728-7"},{"key":"S0269888919000079_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2007.339604"},{"key":"S0269888919000079_ref19","unstructured":"Ng, A. Y. , Harada, D. , & Russell, S. 1999. Policy invariance under reward transformations: theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML), Bratko, I. & Dzeroski, S. (eds), 99, 278\u2013287."},{"volume-title":"Learnability and Cognition","year":"1989","author":"Pinker","key":"S0269888919000079_ref20"},{"key":"S0269888919000079_ref22","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316887"},{"key":"S0269888919000079_ref21","unstructured":"Popov, I. , Heess, N. , Lillicrap, T. , Hafner, R. , Barth-Maron, G. , Vecerik, M. , Lampe, T. , Tassa, Y. , Erez, T. & Riedmiller, M. 2017. Data-efficient deep reinforcement learning for dexterous manipulation. CoRR. https:\/\/arxiv.org\/abs\/1704.03073."}],"container-title":["The Knowledge Engineering Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0269888919000079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T14:42:12Z","timestamp":1767624132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0269888919000079\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":36,"alternative-id":["S0269888919000079"],"URL":"https:\/\/doi.org\/10.1017\/s0269888919000079","relation":{},"ISSN":["0269-8889","1469-8005"],"issn-type":[{"type":"print","value":"0269-8889"},{"type":"electronic","value":"1469-8005"}],"subject":[],"published":{"date-parts":[[2019]]},"article-number":"e13"}}