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arXiv · 2511.09790

A Robust Task-Level Control Architecture for Learned Dynamical Systems

Eshika Pathak·Ahmed Aboudonia·Sandeep Banik·Naira Hovakimyan
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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.

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Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan. 2025-11-12. A Robust Task-Level Control Architecture for Learned Dynamical Systems. https://arxiv.org/abs/2511.09790

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