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.
@article{arxiv.2507.13225,
title = {Signal Temporal Logic Compliant Co-design of Planning and Control},
author = {Manas Sashank Juvvi and Tushar Dilip Kurne and Vaishnavi J and Shishir Kolathaya and Pushpak Jagtap},
journal= {arXiv preprint arXiv:2507.13225},
year = {2025}
}