Signal Temporal Logic Compliant Co-design of Planning and Control
Manas Sashank Juvvi, Tushar Dilip Kurne, Vaishnavi J, Shishir Kolathaya, Pushpak Jagtap
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 spatiotemporal characteristics. Subsequently, we present a sampling-based STL-compliant motion planning strategy 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://youtu.be/xo2cXRYdDPQ and detailed version at https://doi.org/10.48550/arXiv.2507.13225.
BibTeX
@inproceedings{iros2025_signaltemporallo,
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},
booktitle = {IROS 2025},
year = {2025}
}