A Robust Task-Level Control Architecture for Learned Dynamical Systems

[2511.09790] A Robust Task-Level Control Architecture for Learned Dynamical Systems Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2511.09790 (cs) [Submitted on 12 Nov 2025 ( v1 ), last revised 24 Aug 2026 (this version, v2)] Title: A Robust Task-Level Control Architecture for Learned Dynamical Systems Authors: Eshika Pathak , Ahmed Aboudonia , Sandeep Banik , Naira Hovakimyan View PDF HTML (experimental) 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. Comments: Accepted to the 8th Annual Learning for Dynamics & Control Conference (L4DC 2026) Subjects: Robotics (cs.RO) ; Machine Learning (cs.LG); Systems and Control (eess.SY) Report number: PMLR 331:1243-1259 Cite as: 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 Journal reference: Proceedings of Machine Learning Research, PMLR 331:1243-1259, 2026 Submission history From: Eshika Pathak [ view email ] [v1] Wed, 12 Nov 2025 22:45:32 UTC (994 KB) [v2] Mon, 24 Aug 2026 18:42:17 UTC (946 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO < prev | next > new | recent | 2025-11 Change to browse by: cs cs.LG cs.SY eess eess.SY References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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