[2507.12768] AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

Computer Science > Computer Vision and Pattern Recognition arXiv:2507.12768 (cs) [Submitted on 17 Jul 2025 (v1), last revised 6 May 2026 (this version, v2)] Title:AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation Authors:Hengkai Tan, Yao Feng, Xinyi Mao, Shuhe Huang, Guodong Liu, Zhongkai Hao, Hang Su, Jun Zhu View a PDF of the paper titled AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation, by Hengkai Tan and 7 other authors View PDF HTML (experimental) Abstract:Learning generalizable manipulation policies hinges on data, yet robot manipulation data is scarce and often entangled with specific embodiments, making both cross-task and cross-platform transfer difficult. We tackle this challenge with task-agnostic embodiment modeling, which learns embodiment dynamics directly from task-agnostic action data and decouples them from high-level policy learning. By focusing on exploring all feasible actions of the embodiment to capture what is physically feasible and consistent, task-agnostic data takes the form of independent image-action pairs with the potential to cover the entire embodiment workspace, unlike task-specific data, which is sequential and tied to concrete tasks. This data-driven perspective bypasses the limitations of traditional dynamics-based modeling and enables scalable reuse of action data across different tasks. Building on this principle, we introduce AnyPos, a unified pipeline that integrates large-scale automated task-agnosti...