{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/leolani-a-reference-machine-with-a-theory-of","title":"Leolani: a reference machine with a theory of mind for social communication","arxiv_id":"1806.01526","date":"2018-06-05","proceeding":null,"authors":["Piek Vossen","Selene Baez","Lenka Bajčetić","Bram Kraaijeveld"],"abstract":"Our state of mind is based on experiences and what other people tell us. This\nmay result in conflicting information, uncertainty, and alternative facts. We\npresent a robot that models relativity of knowledge and perception within\nsocial interaction following principles of the theory of mind. We utilized\nvision and speech capabilities on a Pepper robot to build an interaction model\nthat stores the interpretations of perceptions and conversations in combination\nwith provenance on its sources. The robot learns directly from what people tell\nit, possibly in relation to its perception. We demonstrate how the robot's\ncommunication is driven by hunger to acquire more knowledge from and on people\nand objects, to resolve uncertainties and conflicts, and to share awareness of\nthe per- ceived environment. Likewise, the robot can make reference to the\nworld and its knowledge about the world and the encounters with people that\nyielded this knowledge.","url_abs":"http://arxiv.org/abs/1806.01526v1","url_pdf":"http://arxiv.org/pdf/1806.01526v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"leolani-a-reference-machine-with-a-theory-of","repo_url":"https://github.com/cltl/pepper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"10-shot-image-generation","task_name":"10-shot image generation"},{"task_slug":"16k","task_name":"16k"},{"task_slug":"2d-cyclist-detection","task_name":"2D Cyclist Detection"},{"task_slug":"2d-human-pose-estimation","task_name":"2D Human Pose Estimation"},{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"3d-human-action-recognition","task_name":"3D Action Recognition"}],"methods":[{"method_slug":"1-bit-adam","method_name":"1-bit Adam"},{"method_slug":"adam","method_name":"Adam"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}