# Representing Robot Task Plans as Robust Logical-Dynamical Systems

arXiv: https://arxiv.org/abs/1908.01896 · HTML: https://mlsanity.com/paper/1908.01896/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [SORNet: Spatial Object-Centric Representations for Sequential Manipulation](https://mlsanity.com/paper/2109.03891/) (2021)
- [Transformers are Adaptable Task Planners](https://mlsanity.com/paper/2207.02442/) (2022)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [RMP2: A Structured Composable Policy Class for Robot Learning](https://mlsanity.com/paper/2103.05922/) (2021)
- [Motion Reasoning for Goal-Based Imitation Learning](https://mlsanity.com/paper/1911.05864/) (2019)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [Continuous-Time Behavior Trees as Discontinuous Dynamical Systems](https://mlsanity.com/paper/2109.01575/) (2021)
- ["The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection](https://mlsanity.com/paper/2205.04172/) (2022)
- [An Extended Convergence Result for Behaviour Tree Controllers](https://mlsanity.com/paper/2308.08994/) (2023)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments](https://mlsanity.com/paper/2404.16138/) (2024)
- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [Contract-Grounded Behavior Tree Synthesis via Coding Agents](https://mlsanity.com/paper/2607.12220/) (2026)

## Abstract (arXiv)

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.