{"type":"ScholarlyArticle","title":"A Survey on Machine Learning Approaches for Modelling Intuitive Physics","authors":[{"name":"Jiafei Duan","slug":"jiafei-duan","given":"Jiafei","family":"Duan","affiliation":"Institute for Infocomm Research , A; * STAR; * STAR {duan"},{"name":"Arijit Dasgupta","slug":"arijit-dasgupta","given":"Arijit","family":"Dasgupta","affiliation":"Department of Mechanical Engineering , National University of Singapore; * STAR; * STAR {duan"},{"name":"Jason Fischer","slug":"jason-fischer","given":"Jason","family":"Fischer","affiliation":"Department of Psychological and Brain Sciences , Johns Hopkins University"},{"name":"Cheston Tan","slug":"cheston-tan","given":"Cheston","family":"Tan","affiliation":"Institute for Infocomm Research , A; Centre for Frontier AI Research , A"}],"abstract":"Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive ability is commonly known as intuitive physics. With advancements in deep learning, there is an increasing interest in building intelligent systems that are capable of performing physical reasoning from a given scene for the purpose of building better AI systems. As a result, many contemporary approaches in modelling intuitive physics for machine cognition have been inspired by literature from cognitive science. Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches. Especially at the intersection of intuitive physics and artificial intelligence, there is a need to make sense of the diverse range of ideas and approaches. Therefore, this paper presents a comprehensive survey of recent advances and techniques in intuitive physics-inspired deep learning approaches for physical reasoning. The survey will first categorize existing deep learning approaches into three facets of physical reasoning before organizing them into three general technical approaches and propose six categorical tasks of the field. Finally, we highlight the challenges of the current field and present some future research directions.","doi":"10.24963/ijcai.2022/763","url":"https://ijcai.org/proceedings/2022/763","pdf":"https://ijcai.org/proceedings/2022/0763.pdf","markdown":"https://ijcai.org/proceedings/2022/763.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/763","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":763,"pages":{"first":5444,"last":5452},"keywords":["Survey Track: Machine Learning","Survey Track: Knowledge Representation and Reasoning","Survey Track: Computer Vision"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987287","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985430","length_sec":643,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ates et al., 2020] Tayfun Ates, Muhammed Samil Ate- soglu, Cagatay Yigit, Ilker Kesen, Mert Kobas, Erkut Er- dem, Aykut Erdem, Tilbe Goksun, and Deniz Yuret. 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