{"ID":2893581,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13225","arxiv_id":"2507.13225","title":"Signal Temporal Logic Compliant Co-design of Planning and Control","abstract":"This work presents a novel co-design strategy that integrates trajectory planning and control to handle STL-based tasks in autonomous robots. The method consists of two phases: $(i)$ learning spatio-temporal motion primitives to encapsulate the inherent robot-specific constraints and $(ii)$ constructing an STL-compliant motion plan from these primitives. Initially, we employ reinforcement learning to construct a library of control policies that perform trajectories described by the motion primitives. Then, we map motion primitives to spatio-temporal characteristics. Subsequently, we present a sampling-based STL-compliant motion planning strategy tailored to meet the STL specification. The proposed model-free approach, which generates feasible STL-compliant motion plans across various environments, is validated on differential-drive and quadruped robots across various STL specifications. Demonstration videos are available at https://tinyurl.com/m6zp7rsm.","short_abstract":"This work presents a novel co-design strategy that integrates trajectory planning and control to handle STL-based tasks in autonomous robots. The method consists of two phases: $(i)$ learning spatio-temporal motion primitives to encapsulate the inherent robot-specific constraints and $(ii)$ constructing an STL-complian...","url_abs":"https://arxiv.org/abs/2507.13225","url_pdf":"https://arxiv.org/pdf/2507.13225v2","authors":"[\"Manas Sashank Juvvi\",\"Tushar Dilip Kurne\",\"Vaishnavi J\",\"Shishir Kolathaya\",\"Pushpak Jagtap\"]","published":"2025-07-17T15:37:24Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","project_urls":"[\"https://tinyurl.com/m6zp7rsm\"]","has_code":false}