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.

Multi-robot exploration is a complex problem that involves multiple robots working in a shared unknown environment. In such scenarios, the safety of the robots is of paramount importance alongside the completion of the exploration task. In this paper, we propose a modular exploration framework that (i) identifies safe frontier targets for multiple robots while taking into account the system dynamics of each robot to ensure collision avoidance with previously unknown obstacles and (ii) ensures that the robots reach their exploration targets while avoiding any obstacles discovered and each other. We employ a scalable approach to generate symbolic controllers for the multi-robot system, utilizing distance functions. We also provide formal guarantees on the safety of the exploration targets and the completion of each exploration run, with the robots avoiding collisions with each other and the obstacles. We test our approach on simulation experiments and a real-world implementation to validate it.