让机器人自动规划符合时间逻辑要求的运动轨迹，提升自主决策能力。
Signal Temporal Logic Compliant Co-design of Planning and Control
- 用强化学习构建适应机器人的运动基元库
- 基于采样生成满足STL规范的可行轨迹
- 适用于轮式与四足机器人，无需模型依赖
本文提出一种新型协同设计策略，将轨迹规划与控制一体化，用于处理自主机器人中的基于信号时序逻辑（STL）的任务。方法分为两个阶段：(i) 通过强化学习构建包含机器人固有约束的时空运动基元库；(ii) 从这些基元中构造满足STL规范的运动规划。首先，利用强化学习训练一系列控制策略以实现由运动基元描述的轨迹；随后，将运动基元映射为时空特性。接着，提出一种面向STL规范的采样式运动规划方法，可适配多种环境与STL规格。该无模型方法在差速驱动和四足机器人上验证，均能生成满足规范的可行轨迹。演示视频见 https://tinyurl.com/m6zp7rsm。
原文摘要 · Abstract (English)
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.
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