A Robust Task-Level Control Architecture for Learned Dynamical Systems

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.

Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2511.09790 [cs.RO]

(or arXiv:2511.09790v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2511.09790 Focus to learn more arXiv-issued DOI via DataCite

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