Thomas Seel
Photo: SCIoI
Thomas Seel’s research aims at synthesizing sensorimotor intelligence in robotic and biomedical systems by exploiting synergies between systems and control theory and machine learning. This includes the development of new methods for artificial motor learning and for information fusion in intelligent sensor networks as well as the application of these methods in soft robotic systems, autonomous vehicles, and biomimetic neuroprostheses. In 2023, he was appointed Head of the the Institute of Mechatronic Systems at Leibniz Universität Hannover. Earlier, he has held the professorship for Intelligent Sensorimotor Systems at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, and he received a PhD degree in Systems and Control Theory from TU Berlin in 2016.
Projects
Thomas Seel is member of:
• Project 11: Dynamical Collective Adaptation and Learning
• Project 23: Control models of perceptual stability
• Project 23: Control models of perceptual stability
Publications +
6984777
Seel
1
apa
50
date
desc
year
20019
https://www.scienceofintelligence.de/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%224XB67GRV%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Schweitzer%20et%20al.%22%2C%22parsedDate%22%3A%222026-03-13%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSchweitzer%2C%20R.%2C%20Seel%2C%20T.%2C%20Raisch%2C%20J.%2C%20%26amp%3B%20Rolfs%2C%20M.%20%282026%29.%20High-fidelity%20but%20hypometric%20spatial%20localization%20of%20afterimages%20across%20saccades.%20%26lt%3Bi%26gt%3BScience%20Advances%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B12%26lt%3B%5C%2Fi%26gt%3B%2811%29%2C%20eaeb0557.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1126%5C%2Fsciadv.aeb0557%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1126%5C%2Fsciadv.aeb0557%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22High-fidelity%20but%20hypometric%20spatial%20localization%20of%20afterimages%20across%20saccades%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Richard%22%2C%22lastName%22%3A%22Schweitzer%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22J%5Cu00f6rg%22%2C%22lastName%22%3A%22Raisch%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Martin%22%2C%22lastName%22%3A%22Rolfs%22%7D%5D%2C%22abstractNote%22%3A%22Humans%20typically%20perceive%20their%20visual%20world%20as%20stable%20and%20continuous%2C%20despite%20frequent%20shifts%20of%20the%20retinotopic%20reference%20frame%20caused%20by%20saccades.%20This%20perceptual%20stability%20is%20paralleled%20by%20afterimage%20movement%20across%20saccades%3A%20Although%20retinotopically%20stable%2C%20afterimages%20appear%20to%20move%20in%20egocentric%20space%20wherever%20the%20eye%20moves.%20To%20investigate%20the%20mechanisms%20underlying%20this%20phenomenon%2C%20we%20tasked%20human%20observers%20to%20localize%20afterimages%20relative%20to%20briefly%20flashed%20probes%20in%20complete%20darkness.%20This%20psychophysical%20tracking%20of%20afterimages%20was%20accompanied%20by%20eye%20tracking%2C%20allowing%20us%20to%20fit%20a%20dedicated%20computational%20model%20to%20accurately%20predict%20afterimage%20movement%20based%20on%20the%20size%20of%20eye%20movements.%20The%20gain%20of%20afterimage%20movement%20was%20significantly%20hypometric%2C%20remained%20unaffected%20by%20postsaccadic%20visual%20feedback%20and%20saccadic%20adaptation%2C%20and%20was%20inversely%20related%20to%20saccade%20gain.%20Assuming%20a%20parsimonious%20framework%20of%20head-centered%20localization%2C%20afterimage%20movement%20is%20driven%20by%20efference-based%2C%20feedforward%20predictions%20of%20visual%20consequences%20of%20saccades%2C%20demonstrating%20the%20phenomenon%5Cu2019s%20usefulness%20for%20studying%20perceptual%20stability.%22%2C%22date%22%3A%222026-03-13%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1126%5C%2Fsciadv.aeb0557%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.science.org%5C%2Fdoi%5C%2F10.1126%5C%2Fsciadv.aeb0557%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-22T12%3A04%3A48Z%22%7D%7D%2C%7B%22key%22%3A%22M8WQ6F56%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Schweitzer%20et%20al.%22%2C%22parsedDate%22%3A%222025-09-29%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSchweitzer%2C%20R.%2C%20Seel%2C%20T.%2C%20Raisch%2C%20J.%2C%20%26amp%3B%20Rolfs%2C%20M.%20%282025%29.%20Early%20visual%20signatures%20and%20benefits%20of%20intra-saccadic%20motion%20streaks.%20%26lt%3Bi%26gt%3BPLOS%20Computational%20Biology%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B21%26lt%3B%5C%2Fi%26gt%3B%289%29%2C%20e1013544.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1371%5C%2Fjournal.pcbi.1013544%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1371%5C%2Fjournal.pcbi.1013544%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Early%20visual%20signatures%20and%20benefits%20of%20intra-saccadic%20motion%20streaks%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Richard%22%2C%22lastName%22%3A%22Schweitzer%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22J%5Cu00f6rg%22%2C%22lastName%22%3A%22Raisch%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Martin%22%2C%22lastName%22%3A%22Rolfs%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Tim%20Christian%22%2C%22lastName%22%3A%22Kietzmann%22%7D%5D%2C%22abstractNote%22%3A%22Eye%20movements%20routinely%20induce%20motion%20streaks%20as%20they%20shift%20visual%20projections%20across%20the%20retina%20at%20high%20speeds.%20To%20investigate%20the%20visual%20consequences%20of%20intra-saccadic%20motion%20streaks%2C%20we%20co-registered%20eye%20tracking%20and%20EEG%20while%20gaze-contingently%20shifting%20target%20objects%20during%20saccades%2C%20presenting%20either%20continuous%2C%20%5Cu2018streaky%5Cu2019%20or%20apparent%2C%20step-like%20motion%20in%20four%20directions.%20We%20found%20significant%20reductions%20of%20secondary%20saccade%20latency%2C%20as%20well%20as%20improved%20decoding%20of%20the%20post-saccadic%20target%20location%20from%20the%20EEG%20signal%20when%20motion%20streaks%20were%20available.%20These%20signals%20arose%20as%20early%20as%2050%20ms%20after%20saccade%20offset%20and%20had%20a%20clear%20occipital%20topography.%20Using%20a%20physiologically%20plausible%20visual%20processing%20model%2C%20we%20provide%20evidence%20that%20the%20target%5Cu2019s%20motion%20trajectory%20is%20coded%20in%20orientation-selective%20channels%20and%20that%20speed%20of%20gaze%20correction%20was%20linked%20to%20the%20visual%20dynamics%20arising%20from%20the%20combination%20of%20saccadic%20and%20target%20motion%2C%20providing%20a%20parsimonious%20explanation%20of%20the%20behavioral%20benefits%20of%20intra-saccadic%20motion%20streaks.%22%2C%22date%22%3A%222025-9-29%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1371%5C%2Fjournal.pcbi.1013544%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdx.plos.org%5C%2F10.1371%5C%2Fjournal.pcbi.1013544%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%221553-7358%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-22T12%3A04%3A39Z%22%7D%7D%2C%7B%22key%22%3A%22SB55V36C%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Schweitzer%20et%20al.%22%2C%22parsedDate%22%3A%222025%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSchweitzer%2C%20R.%2C%20Doering%2C%20M.%2C%20Seel%2C%20T.%2C%20Raisch%2C%20J.%2C%20%26amp%3B%20Rolfs%2C%20M.%20%282025%29.%20Saccadic%20omission%20revisited%3A%20What%20saccade-induced%20smear%20looks%20like.%20%26lt%3Bi%26gt%3BPsychological%20Review%26lt%3B%5C%2Fi%26gt%3B.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1037%5C%2Frev0000574%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1037%5C%2Frev0000574%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Saccadic%20omission%20revisited%3A%20What%20saccade-induced%20smear%20looks%20like%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Richard%22%2C%22lastName%22%3A%22Schweitzer%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mara%22%2C%22lastName%22%3A%22Doering%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22J%5Cu00f6rg%22%2C%22lastName%22%3A%22Raisch%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Martin%22%2C%22lastName%22%3A%22Rolfs%22%7D%5D%2C%22abstractNote%22%3A%22During%20active%20visual%20exploration%2C%20saccadic%20eye%20movements%20rapidly%20shift%20the%20visual%20image%20across%20the%20human%20retina.%20Although%20these%20high-speed%20shifts%20occur%20at%20a%20high%20rate%20and%20introduce%20considerable%20amounts%20of%20motion%20smear%20during%20natural%20vision%2C%20our%20perceptual%20experience%20is%20oblivious%20to%20it%5Cu2014a%20phenomenon%20known%20as%20saccadic%20omission.%20Using%20tachistoscopic%20displays%20of%20natural%20scenes%2C%20we%20rendered%20saccade-induced%20smear%20highly%20conspicuous.%20By%20manipulating%20perisaccadic%20display%20durations%2C%20we%20studied%20the%20dynamics%20of%20smear%20in%20a%20time-resolved%20manner%2C%20assessing%20discrimination%20performance%20of%20smeared%20scenes%2C%20as%20well%20as%20smear%20amount%20and%20orientation.%20Both%20measures%20showed%20distinctive%2C%20U-shaped%20time%20courses%20throughout%20the%20saccade%2C%20indicating%20that%20generation%20and%20reduction%20of%20smear%20occurred%20during%20saccades.%20Moreover%2C%20low%20spatial%20frequencies%20and%20orientations%20parallel%20to%20the%20direction%20of%20the%20ongoing%20saccade%20were%20identified%20as%20the%20predominant%20visual%20features%20accessible%20in%20motion%20smear.%20We%20explain%20these%20findings%20using%20computational%20models%20that%20assume%20no%20more%20than%20saccadic%20velocity%20and%20human%20contrast%20sensitivity%20profiles%20and%20present%20a%20motion-filter%20model%20capable%20of%20predicting%20observers%5Cu2019%20perceived%20amount%20of%20smear%20based%20on%20their%20eyes%5Cu2019%20trajectories%2C%20suggesting%20a%20direct%20link%20between%20perceptual%20and%20saccade%20dynamics.%20Replays%20of%20the%20visual%20consequences%20of%20saccades%20during%20fixation%20led%20to%20virtually%20identical%20results%20as%20actively%20making%20saccades%2C%20whereas%20the%20additional%20simulation%20of%20perisaccadic%20contrast%20suppression%20heavily%20reduced%20this%20similarity%2C%20providing%20strong%20evidence%20that%20this%20suppression%20explained%20neither%20our%20findings%20nor%20the%20phenomenon%20of%20omission.%20Saccadic%20omission%20of%20motion%20smear%20may%20be%20understood%20as%20emerging%20naturally%20from%20the%20interplay%20of%20the%20retinal%20consequences%20of%20saccades%20and%20early%20visual%20processing.%20%28PsycInfo%20Database%20Record%20%28c%29%202025%20APA%2C%20all%20rights%20reserved%29%22%2C%22date%22%3A%222025%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1037%5C%2Frev0000574%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%221939-1471%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-22T12%3A00%3A12Z%22%7D%7D%2C%7B%22key%22%3A%22BD3LA9AD%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Lehmann%20et%20al.%22%2C%22parsedDate%22%3A%222023-12-13%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BLehmann%2C%20D.%2C%20Drebinger%2C%20P.%2C%20Seel%2C%20T.%2C%20%26amp%3B%20Raisch%2C%20J.%20%282023%29.%20Data-Driven%20Dynamic%20Input%20Transfer%20for%20Learning%20Control%20in%20Multi-Agent%20Systems%20with%20Heterogeneous%20Unknown%20Dynamics.%20%26lt%3Bi%26gt%3B2023%2062nd%20IEEE%20Conference%20on%20Decision%20and%20Control%20%28CDC%29%26lt%3B%5C%2Fi%26gt%3B%2C%202358%26%23x2013%3B2365.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FCDC49753.2023.10383433%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FCDC49753.2023.10383433%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Data-Driven%20Dynamic%20Input%20Transfer%20for%20Learning%20Control%20in%20Multi-Agent%20Systems%20with%20Heterogeneous%20Unknown%20Dynamics%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dustin%22%2C%22lastName%22%3A%22Lehmann%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Philipp%22%2C%22lastName%22%3A%22Drebinger%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22J%5Cu00f6rg%22%2C%22lastName%22%3A%22Raisch%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22proceedingsTitle%22%3A%222023%2062nd%20IEEE%20Conference%20on%20Decision%20and%20Control%20%28CDC%29%22%2C%22conferenceName%22%3A%222023%2062nd%20IEEE%20Conference%20on%20Decision%20and%20Control%20%28CDC%29%22%2C%22date%22%3A%222023-12-13%22%2C%22eventPlace%22%3A%22%22%2C%22DOI%22%3A%2210.1109%5C%2FCDC49753.2023.10383433%22%2C%22ISBN%22%3A%22979-8-3503-0124-3%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fieeexplore.ieee.org%5C%2Fdocument%5C%2F10383433%5C%2F%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-29T10%3A40%3A20Z%22%7D%7D%2C%7B%22key%22%3A%22XX6XEM8R%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Meindl%20et%20al.%22%2C%22parsedDate%22%3A%222022-07-12%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BMeindl%2C%20M.%2C%20Lehmann%2C%20D.%2C%20%26amp%3B%20Seel%2C%20T.%20%282022%29.%20Bridging%20Reinforcement%20Learning%20and%20Iterative%20Learning%20Control%3A%20Autonomous%20Motion%20Learning%20for%20Unknown%2C%20Nonlinear%20Dynamics.%20%26lt%3Bi%26gt%3BFrontiers%20in%20Robotics%20and%20AI%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B9%26lt%3B%5C%2Fi%26gt%3B%2C%20793512.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3389%5C%2Ffrobt.2022.793512%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3389%5C%2Ffrobt.2022.793512%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Bridging%20Reinforcement%20Learning%20and%20Iterative%20Learning%20Control%3A%20Autonomous%20Motion%20Learning%20for%20Unknown%2C%20Nonlinear%20Dynamics%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Meindl%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dustin%22%2C%22lastName%22%3A%22Lehmann%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%5D%2C%22abstractNote%22%3A%22This%20work%20addresses%20the%20problem%20of%20reference%20tracking%20in%20autonomously%20learning%20robots%20with%20unknown%2C%20nonlinear%20dynamics.%20Existing%20solutions%20require%20model%20information%20or%20extensive%20parameter%20tuning%2C%20and%20have%20rarely%20been%20validated%20in%20real-world%20experiments.%20We%20propose%20a%20learning%20control%20scheme%20that%20learns%20to%20approximate%20the%20unknown%20dynamics%20by%20a%20Gaussian%20Process%20%28GP%29%2C%20which%20is%20used%20to%20optimize%20and%20apply%20a%20feedforward%20control%20input%20on%20each%20trial.%20Unlike%20existing%20approaches%2C%20the%20proposed%20method%20neither%20requires%20knowledge%20of%20the%20system%20states%20and%20their%20dynamics%20nor%20knowledge%20of%20an%20effective%20feedback%20control%20structure.%20All%20algorithm%20parameters%20are%20chosen%20automatically%2C%20i.e.%20the%20learning%20method%20works%20plug%20and%20play.%20The%20proposed%20method%20is%20validated%20in%20extensive%20simulations%20and%20real-world%20experiments.%20In%20contrast%20to%20most%20existing%20work%2C%20we%20study%20learning%20dynamics%20for%20more%20than%20one%20motion%20task%20as%20well%20as%20the%20robustness%20of%20performance%20across%20a%20large%20range%20of%20learning%20parameters.%20The%20method%5Cu2019s%20plug%20and%20play%20applicability%20is%20demonstrated%20by%20experiments%20with%20a%20balancing%20robot%2C%20in%20which%20the%20proposed%20method%20rapidly%20learns%20to%20track%20the%20desired%20output.%20Due%20to%20its%20model-agnostic%20and%20plug%20and%20play%20properties%2C%20the%20proposed%20method%20is%20expected%20to%20have%20high%20potential%20for%20application%20to%20a%20large%20class%20of%20reference%20tracking%20problems%20in%20systems%20with%20unknown%2C%20nonlinear%20dynamics.%22%2C%22date%22%3A%222022-7-12%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.3389%5C%2Ffrobt.2022.793512%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.frontiersin.org%5C%2Farticles%5C%2F10.3389%5C%2Ffrobt.2022.793512%5C%2Ffull%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222296-9144%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-29T10%3A40%3A20Z%22%7D%7D%2C%7B%22key%22%3A%22XG88H3GI%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Meindl%20et%20al.%22%2C%22parsedDate%22%3A%222022%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BMeindl%2C%20M.%2C%20Molinari%2C%20F.%2C%20Lehmann%2C%20D.%2C%20%26amp%3B%20Seel%2C%20T.%20%282022%29.%20Collective%20Iterative%20Learning%20Control%3A%20Exploiting%20Diversity%20in%20Multi-Agent%20Systems%20for%20Reference%20Tracking%20Tasks.%20%26lt%3Bi%26gt%3BIEEE%20Transactions%20on%20Control%20Systems%20Technology%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B30%26lt%3B%5C%2Fi%26gt%3B%284%29%2C%201390%26%23x2013%3B1402.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FTCST.2021.3109646%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FTCST.2021.3109646%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Collective%20Iterative%20Learning%20Control%3A%20Exploiting%20Diversity%20in%20Multi-Agent%20Systems%20for%20Reference%20Tracking%20Tasks%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Meindl%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Fabio%22%2C%22lastName%22%3A%22Molinari%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dustin%22%2C%22lastName%22%3A%22Lehmann%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%227%5C%2F2022%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1109%5C%2FTCST.2021.3109646%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fieeexplore.ieee.org%5C%2Fdocument%5C%2F9537696%5C%2F%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%221063-6536%2C%201558-0865%2C%202374-0159%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-29T10%3A40%3A20Z%22%7D%7D%2C%7B%22key%22%3A%2259PJQBSM%22%2C%22library%22%3A%7B%22id%22%3A6984777%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Meindl%20et%20al.%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BMeindl%2C%20M.%2C%20Molinari%2C%20F.%2C%20Raisch%2C%20J.%2C%20%26amp%3B%20Seel%2C%20T.%20%282020%29.%20Overcoming%20Output%20Constraints%20in%20Iterative%20Learning%20Control%20Systems%20by%20Reference%20Adaptation.%20%26lt%3Bi%26gt%3BIFAC-PapersOnLine%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B53%26lt%3B%5C%2Fi%26gt%3B%282%29%2C%201480%26%23x2013%3B1486.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1016%5C%2Fj.ifacol.2020.12.1938%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1016%5C%2Fj.ifacol.2020.12.1938%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Overcoming%20Output%20Constraints%20in%20Iterative%20Learning%20Control%20Systems%20by%20Reference%20Adaptation%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Meindl%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Fabio%22%2C%22lastName%22%3A%22Molinari%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22J%5Cu00f6rg%22%2C%22lastName%22%3A%22Raisch%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Seel%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222020%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1016%5C%2Fj.ifacol.2020.12.1938%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Flinkinghub.elsevier.com%5C%2Fretrieve%5C%2Fpii%5C%2FS2405896320325647%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%2224058963%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222026-06-22T11%3A53%3A23Z%22%7D%7D%5D%7D
Schweitzer, R., Seel, T., Raisch, J., & Rolfs, M. (2026). High-fidelity but hypometric spatial localization of afterimages across saccades. Science Advances, 12(11), eaeb0557. https://doi.org/10.1126/sciadv.aeb0557
Schweitzer, R., Seel, T., Raisch, J., & Rolfs, M. (2025). Early visual signatures and benefits of intra-saccadic motion streaks. PLOS Computational Biology, 21(9), e1013544. https://doi.org/10.1371/journal.pcbi.1013544
Schweitzer, R., Doering, M., Seel, T., Raisch, J., & Rolfs, M. (2025). Saccadic omission revisited: What saccade-induced smear looks like. Psychological Review. https://doi.org/10.1037/rev0000574
Lehmann, D., Drebinger, P., Seel, T., & Raisch, J. (2023). Data-Driven Dynamic Input Transfer for Learning Control in Multi-Agent Systems with Heterogeneous Unknown Dynamics. 2023 62nd IEEE Conference on Decision and Control (CDC), 2358–2365. https://doi.org/10.1109/CDC49753.2023.10383433
Meindl, M., Lehmann, D., & Seel, T. (2022). Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics. Frontiers in Robotics and AI, 9, 793512. https://doi.org/10.3389/frobt.2022.793512
Meindl, M., Molinari, F., Lehmann, D., & Seel, T. (2022). Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks. IEEE Transactions on Control Systems Technology, 30(4), 1390–1402. https://doi.org/10.1109/TCST.2021.3109646
Meindl, M., Molinari, F., Raisch, J., & Seel, T. (2020). Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation. IFAC-PapersOnLine, 53(2), 1480–1486. https://doi.org/10.1016/j.ifacol.2020.12.1938
Bernstein Network – March 2026 - The ghosts we see: How afterimages reveal why the world appears stable
The Telegraph – March 2026 - Scientists reveal the secret of ghost sightings
Earth – March 2026 - How your brain predicts movement to keep the world stable
The Debrief – March 2026 - As the Brain Stabilizes Dizzying Eye Movements, These Ghostly Images Appear—Now Scientists Know Why
Neuroscience News – March 2026 - Ghost in the Machine: Brain Predicts Images Before We See Them
plawiuk – March2026 - The ghosts we see How afterimages reveal why the world appears stable
DISCOVER – March 2026 - How Our Brains Predict Eye Movements — and Why Afterimages Don’t Always Line Up
Medical Express – March 2026 - The ghosts we see: Afterimages provide clues to how our brains perceive a stable environment
