{"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/multimodal-hierarchical-dirichlet-process","title":"Multimodal Hierarchical Dirichlet Process-based Active Perception","arxiv_id":"1510.00331","date":"2015-10-01","proceeding":null,"authors":["Tadahiro Taniguchi","Toshiaki Takano","Ryo Yoshino"],"abstract":"In this paper, we propose an active perception method for recognizing object\ncategories based on the multimodal hierarchical Dirichlet process (MHDP). The\nMHDP enables a robot to form object categories using multimodal information,\ne.g., visual, auditory, and haptic information, which can be observed by\nperforming actions on an object. However, performing many actions on a target\nobject requires a long time. In a real-time scenario, i.e., when the time is\nlimited, the robot has to determine the set of actions that is most effective\nfor recognizing a target object. We propose an MHDP-based active perception\nmethod that uses the information gain (IG) maximization criterion and lazy\ngreedy algorithm. We show that the IG maximization criterion is optimal in the\nsense that the criterion is equivalent to a minimization of the expected\nKullback--Leibler divergence between a final recognition state and the\nrecognition state after the next set of actions. However, a straightforward\ncalculation of IG is practically impossible. Therefore, we derive an efficient\nMonte Carlo approximation method for IG by making use of a property of the\nMHDP. We also show that the IG has submodular and non-decreasing properties as\na set function because of the structure of the graphical model of the MHDP.\nTherefore, the IG maximization problem is reduced to a submodular maximization\nproblem. This means that greedy and lazy greedy algorithms are effective and\nhave a theoretical justification for their performance. We conducted an\nexperiment using an upper-torso humanoid robot and a second one using synthetic\ndata. The experimental results show that the method enables the robot to select\na set of actions that allow it to recognize target objects quickly and\naccurately. The results support our theoretical outcomes.","url_abs":"http://arxiv.org/abs/1510.00331v3","url_pdf":"http://arxiv.org/pdf/1510.00331v3.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":"multimodal-hierarchical-dirichlet-process","repo_url":"https://github.com/tanichu/data-active-perception-hmdp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.00331","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}