Signal Temporal Logic Compliant Co-design of Planning and Control

2025-07-17

AI Summary

Autonomous robots face challenges in decoupling trajectory planning from control and ensuring formal verification when executing Signal Temporal Logic (STL) tasks in complex environments. Method: This paper proposes a model-free planning–control co-design framework. It offline constructs a spatiotemporal motion primitive library via reinforcement learning and establishes a verifiable mapping from primitives to STL spatiotemporal semantics. Online, it integrates sampling-based STL-satisfying planning to synthesize safe motion sequences adhering to diverse temporal constraints. Contribution/Results: The framework requires no system dynamics model and enables end-to-end behavioral verification. Experiments on differential-drive and quadrupedal robot platforms demonstrate its effectiveness and generalization capability in dynamic obstacle avoidance and multi-objective temporal tasks.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 https://tinyurl.com/m6zp7rsm.

Problem

• Integrate trajectory planning and control for STL tasks
• Learn spatio-temporal motion primitives for robot constraints
• Generate STL-compliant motion plans for autonomous robots

Innovation

• Co-design integrates planning and control
• Reinforcement learning builds control policies
• Sampling-based STL-compliant motion planning