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 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 this https URL.
Abstract (translated)
这项工作提出了一种新颖的协同设计策略，该策略将轨迹规划与控制相结合，以处理自主机器人中基于STL的任务。此方法包含两个阶段：$(i)$ 学习时空运动原语来封装特定于机器人的内在约束；$(ii)$ 从这些原语构建符合STL规范的运动计划。 初始阶段，我们利用强化学习建立了一组控制策略库，以执行由运动原语描述的轨迹。然后，我们将运动原语映射到时空特征上。随后，我们提出了一种基于采样的、符合STL规范的运动规划策略，以满足特定的STL要求。所提出的无模型方法能够生成适用于各种环境中的可行且符合STL规范的运动计划，并已在差分驱动和四足机器人上进行了跨不同STL规格的验证。 演示视频可在以下链接访问：[此URL]（请将"[此URL]"替换为实际提供的网址）。
URL
https://arxiv.org/abs/2507.13225
