A Robust Task-Level Control Architecture for Learned Dynamical Systems

Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1243-1259, 2026.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-pathak26a, title = {A Robust Task-Level Control Architecture for Learned Dynamical Systems}, author = {Pathak, Eshika and Aboudonia, Ahmed and Banik, Sandeep and Hovakimyan, Naira}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1243--1259}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/pathak26a/pathak26a.pdf}, url = {https://proceedings.mlr.press/v331/pathak26a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T A Robust Task-Level Control Architecture for Learned Dynamical Systems %A Eshika Pathak %A Ahmed Aboudonia %A Sandeep Banik %A Naira Hovakimyan %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-pathak26a %I PMLR %P 1243--1259 %U https://proceedings.mlr.press/v331/pathak26a.html %V 331 %X 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.
APA
Pathak, E., Aboudonia, A., Banik, S. & Hovakimyan, N.. (2026). A Robust Task-Level Control Architecture for Learned Dynamical Systems. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1243-1259 Available from https://proceedings.mlr.press/v331/pathak26a.html.

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