Скачать или смотреть Signal Temporal Logic Compliant Co design of Planning and Control

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 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. Detailed version of the paper is available at https://arxiv.org/abs/2507.13225