{"type":"ScholarlyArticle","title":"MARS: Multimodal Active Robotic Sensing for Articulated Characterization","authors":[{"name":"Hongliang Zeng","slug":"hongliang-zeng","given":"Hongliang","family":"Zeng","affiliation":"South China University of Technology , Guangzhou , China"},{"name":"Ping Zhang","slug":"ping-zhang","given":"Ping","family":"Zhang","affiliation":"South China University of Technology , Guangzhou , China"},{"name":"Chengjiong Wu","slug":"chengjiong-wu","given":"Chengjiong","family":"Wu","affiliation":"South China University of Technology , Guangzhou , China"},{"name":"Jiahua Wang","slug":"jiahua-wang","given":"Jiahua","family":"Wang","affiliation":"South China University of Technology , Guangzhou , China"},{"name":"Tingyu Ye","slug":"tingyu-ye","given":"Tingyu","family":"Ye","affiliation":"South China University of Technology , Guangzhou , China"},{"name":"Fang Li","slug":"fang-li","given":"Fang","family":"Li","affiliation":"South China University of Technology , Guangzhou , China"}],"abstract":"Precise perception of articulated objects is vital for empowering service robots. Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios. To address these limitations, we introduce MARS, a novel framework for articulated object characterization. It features a multi-modal fusion module utilizing multi-scale RGB features to enhance point cloud features, coupled with reinforcement learning-based active sensing for autonomous optimization of observation viewpoints. In experiments conducted with various articulated object instances from the PartNet-Mobility dataset, our method outperformed current state-of-the-art methods in joint parameter estimation accuracy. Additionally, through active sensing, MARS further reduces errors, demonstrating enhanced efficiency in handling suboptimal viewpoints. Furthermore, our method effectively generalizes to real-world articulated objects, enhancing robot interactions. Code is available at https://github.com/robhlzeng/MARS.","doi":"10.24963/ijcai.2024/181","url":"https://ijcai.org/proceedings/2024/181","pdf":"https://ijcai.org/proceedings/2024/0181.pdf","markdown":"https://ijcai.org/proceedings/2024/181.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/181","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":181,"pages":{"first":1634,"last":1642},"keywords":["Robotics: ROB: Perception","Computer Vision: CV: Multimodal learning","Computer Vision: CV: 3D computer vision","Robotics: ROB: Manipulation"],"videos":[],"references":[{"position":1,"raw":"[Abbatematteo et al., 2019] Ben Abbatematteo, Stefanie Tellex, and George Konidaris. 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