Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives

[2512.22983] Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2512.22983 (cs) [Submitted on 28 Dec 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives Authors: Shuanghao Bai , Wenxuan Song , Jiayi Chen , Yuheng Ji , Zhide Zhong , Jin Yang , Han Zhao , Wanqi Zhou , Zhe Li , Pengxiang Ding , Cheng Chi , Chang Xu , Xiaolong Zheng , Donglin Wang , Haoang Li , Shanghang Zhang , Badong Chen View a PDF of the paper titled Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives, by Shuanghao Bai and 16 other authors View PDF HTML (experimental) Abstract: Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic manipulation remaining one of the most challenging embodied tasks. Its difficulty lies in integrating perception, semantic understanding, task reasoning, physically grounded action generation, and reliable execution. This survey examines robotic manipulation from an algorithmic perspective and organizes recent learning-based approaches through a unified abstraction of high-level planning and low-level action modeling. At the high level, we extend the classical notion of task planning to include reasoning over language, code, affordances, geometric constraints, and 3D representations. At the low level, we present a learning-paradigm-oriented taxonomy of learning-based action models, covering input modeling, latent learning, and policy learning. Within this abstraction, foundation models contribute either by generating structured planning artifacts that are instantiated as constraints or latent inputs for downstream action generation, or by directly modeling executable actions and trajectories. Finally, we summarize open challenges and future directions related to scalability, generalization, data efficiency, multimodal physical interaction, and safety. Together, this survey provides a structured view of the design space and emerging trends in foundation models for robotic manipulation. Comments: This work is a re-architected core derived from the full survey ( arXiv:2510.10903 ), refined to highlight the most central themes and representative studies Subjects: Robotics (cs.RO) Cite as: arXiv:2512.22983 [cs.RO] (or arXiv:2512.22983v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2512.22983 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shuanghao Bai [ view email ] [v1] Sun, 28 Dec 2025 16:05:38 UTC (13,459 KB) [v2] Sat, 15 Aug 2026 11:07:27 UTC (15,500 KB) Full-text links: Access Paper: View a PDF of the paper titled Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives, by Shuanghao Bai and 16 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2025-12 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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