待翻译：Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2606.00059v1 Announce Type: new Abstract: Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge and hand-crafted signal design to respect hardware safety constraints, limiting their generalizability. We propose a reinforcement learning (RL) agent that learns optimal excitation signals for a Quanser Aero 2 testbed while autonomously enforcing safety constraints through reward shaping. Evaluated across 10 independent training seeds, our comprehensive agent achieves competitive estimation accuracy across all three identified parameters, outperforming classical baselines while incurring only 0.75% safety violations.
AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
[2606.00059] Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems [Submitted on 19 May 2026] Title:Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems View a PDF of the paper titled Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems, by Julian Langschwert and Georg Schaefer and Jakob Rehrl and Stefan Huber and Simon Hirlaender View PDF HTML (experimental) Abstract:Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge and hand-crafted signal design to respect hardware safety constraints, limiting their generalizability. We propose a reinforcement learning (RL) agent that learns optimal excitation signals for a Quanser Aero 2 testbed while autonomously enforcing safety constraints through reward shaping. Evaluated across 10 independent training seeds, our comprehensive agent achieves competitive estimation accuracy across all three identified parameters, outperforming classical baselines while incurring only 0.75% safety violations. Comments: Accepted at DEXA AI4IP 2026 Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2606.00059 [cs.RO] (or arXiv:2606.00059v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2606.00059 arXiv-issued DOI via DataCite Submission history From: Julian Langschwert [view email] [v1] Tue, 19 May 2026 11:39:49 UTC (16 KB) Full-text links: Access Paper: View a PDF of the paper titled Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems, by Julian Langschwert and Georg Schaefer and Jakob Rehrl and Stefan Huber and Simon Hirlaender View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-06 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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?)