# Representing Robot Task Plans as Robust Logical-Dynamical Systems

## TL;DR

This paper introduces Robust Logical-Dynamical Systems (RLDS), a framework that combines task representation and theoretical guarantees to enable robust, reactive robot behaviors that can be automatically constructed from simple plans.

## Contribution

The paper presents RLDS, a novel framework that integrates logical task plans with dynamical systems, providing robustness and automatic construction from simple task sequences.

## Key findings

- RLDS achieves robust, reactive behaviors in dynamic environments.
- Combining RLDS with planning improves performance under adversarial conditions.
- The framework can be automatically generated from simple sequential plans.

## Abstract

It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal condition. We propose a framework which we call Robust Logical-Dynamical Systems (RLDS), which combines the advantages of task representations like behavior trees with theoretical guarantees on performance. RLDS can also be constructed automatically from simple sequential task plans and will still achieve robust, reactive behavior in dynamic real-world environments. In this work, we describe both our proposed framework and a case study on a simple household manipulation task, with examples for how specific pieces can be implemented to achieve robust behavior. Finally, we show how in the context of these manipulation tasks, a combination of an RLDS with planning can achieve better results under adversarial conditions.