{"ID":2864295,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23823","arxiv_id":"2509.23823","title":"Control Your Robot: A Unified System for Robot Control and Policy Deployment","abstract":"Cross-platform robot control remains difficult because hardware interfaces, data formats, and control paradigms vary widely, which fragments toolchains and slows deployment. To address this, we present Control Your Robot, a modular, general-purpose framework that unifies data collection and policy deployment across diverse platforms. The system reduces fragmentation through a standardized workflow with modular design, unified APIs, and a closed-loop architecture. It supports flexible robot registration, dual-mode control with teleoperation and trajectory playback, and seamless integration from multimodal data acquisition to inference. Experiments on single-arm and dual-arm systems show efficient, low-latency data collection and effective support for policy learning with imitation learning and vision-language-action models. Policies trained on data gathered by Control Your Robot match expert demonstrations closely, indicating that the framework enables scalable and reproducible robot learning across platforms.","short_abstract":"Cross-platform robot control remains difficult because hardware interfaces, data formats, and control paradigms vary widely, which fragments toolchains and slows deployment. To address this, we present Control Your Robot, a modular, general-purpose framework that unifies data collection and policy deployment across div...","url_abs":"https://arxiv.org/abs/2509.23823","url_pdf":"https://arxiv.org/pdf/2509.23823v2","authors":"[\"Tian Nian\",\"Weijie Ke\",\"Shaolong Zhu\",\"Bingshan Hu\"]","published":"2025-09-28T11:51:29Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}