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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.13225 |
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| _version_ | 1866908466170298368 |
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| author | Juvvi, Manas Sashank Kurne, Tushar Dilip J, Vaishnavi Kolathaya, Shishir Jagtap, Pushpak |
| author_facet | Juvvi, Manas Sashank Kurne, Tushar Dilip J, Vaishnavi Kolathaya, Shishir Jagtap, Pushpak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signal Temporal Logic Compliant Co-design of Planning and Control Juvvi, Manas Sashank Kurne, Tushar Dilip J, Vaishnavi Kolathaya, Shishir Jagtap, Pushpak Robotics 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. |
| title | Signal Temporal Logic Compliant Co-design of Planning and Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.13225 |