# A Robust Task-Level Control Architecture for Learned Dynamical Systems

arXiv: https://arxiv.org/abs/2511.09790 · HTML: https://mlsanity.com/paper/2511.09790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 16 of 16)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control](https://mlsanity.com/paper/2106.12782/) (2021)
- [ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows](https://mlsanity.com/paper/2010.13129/) (2020)
- [Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems](https://mlsanity.com/paper/2005.13143/) (2020)
- [Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems](https://mlsanity.com/paper/2003.11500/) (2020)
- [Learning Riemannian Stable Dynamical Systems via Diffeomorphisms](https://mlsanity.com/paper/2211.03169/) (2022)
- [L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors](https://mlsanity.com/paper/2004.00152/) (2020)
- [Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies](https://mlsanity.com/paper/2309.01884/) (2023)
- [Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees](https://mlsanity.com/paper/2308.00186/) (2023)
- [Learning Deep Dynamical Systems using Stable Neural ODEs](https://mlsanity.com/paper/2404.10622/) (2024)
- [An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems](https://mlsanity.com/paper/2003.11290/) (2020)
- [Learning Lyapunov-Stable Polynomial Dynamical Systems through Imitation](https://mlsanity.com/paper/2310.20605/) (2023)
- [The sensorimotor loop as a dynamical system: How regular motion primitives may emerge from self-organized limit cycles](https://mlsanity.com/paper/1511.04338/) (2015)
- [Adaptive Robust Controller for handling Unknown Uncertainty of Robotic Manipulators](https://mlsanity.com/paper/2406.14338/) (2024)
- [Humanoid Robot Acrobatics Utilizing Complete Articulated Rigid Body Dynamics](https://mlsanity.com/paper/2508.08258/) (2025)

## Abstract (arXiv)

Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space of robotic systems. However, realizing generated motion plans is often compromised by a "task-execution mismatch", where unmodeled dynamics, persistent disturbances, and system latency cause the robot's task-space state to diverge from the desired state. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.