{"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/learning-social-affordance-for-human-robot","title":"Learning Social Affordance for Human-Robot Interaction","arxiv_id":"1604.03692","date":"2016-04-13","proceeding":null,"authors":["Tianmin Shu","M. S. Ryoo","Song-Chun Zhu"],"abstract":"In this paper, we present an approach for robot learning of social affordance\nfrom human activity videos. We consider the problem in the context of\nhuman-robot interaction: Our approach learns structural representations of\nhuman-human (and human-object-human) interactions, describing how body-parts of\neach agent move with respect to each other and what spatial relations they\nshould maintain to complete each sub-event (i.e., sub-goal). This enables the\nrobot to infer its own movement in reaction to the human body motion, allowing\nit to naturally replicate such interactions.\n  We introduce the representation of social affordance and propose a generative\nmodel for its weakly supervised learning from human demonstration videos. Our\napproach discovers critical steps (i.e., latent sub-events) in an interaction\nand the typical motion associated with them, learning what body-parts should be\ninvolved and how. The experimental results demonstrate that our Markov Chain\nMonte Carlo (MCMC) based learning algorithm automatically discovers\nsemantically meaningful interactive affordance from RGB-D videos, which allows\nus to generate appropriate full body motion for an agent.","url_abs":"http://arxiv.org/abs/1604.03692v2","url_pdf":"http://arxiv.org/pdf/1604.03692v2.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":[],"tasks":[{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[{"slug":"hhoi","name":"HHOI","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.03692","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}