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DTSTAMP:20251001T152503Z
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DTSTART;TZID=Europe/London:20251001T110000
DTEND;TZID=Europe/London:20251001T121500
DESCRIPTION:IEEE UK and Ireland – RAS Chapter\nEarly-Career Researcher Se
 minar\n\n[More information: Link](https://drive.google.com/file/d/1HZjkgMN
 mkGN5TDYdX5SAZk_Xt0r6sNHn/view?usp=sharing)\n\nSpeaker: Dr. Fernando E. Ca
 sado\n\nResearch Associate\, Personal Robotics Lab (PRL)\, Imperial Colleg
 e London\n\nFernando is a Research Associate at the Personal Robotics Lab\
 , Imperial College London\, UK. He received his B.Sc.\, M.Sc.\, and Ph.D. 
 in Computer Science and Artificial Intelligence from the University of San
 tiago de Compostela\, Spain\, between 2017 and 2022. His research interest
 s focus on privacy-aware\, multi-robot and multi-user machine learning for
  modelling\, adaptation\, and personalisation of robotic behaviours in tru
 stworthy human–robot interaction.\n\nAbstract:\nAssistive robots can gre
 atly enhance the independence and quality of life of those most in need. H
 owever\, developing intelligent robotic systems that learn to help and ada
 pt to users remains challenging. Unlike other AI applications\, assistive 
 robotics faces data scarcity due to the high cost of real-world data colle
 ction and the need for human-in-the-loop learning and personalisation. As 
 personal robots are deployed in homes\, vast amounts of sensitive interact
 ion data (such as gaze\, facial expressions\, and environmental context) w
 ill become available. Effectively leveraging this data requires machine le
 arning strategies that integrate the user while protecting their privacy. 
 In this seminar\, we will explore techniques such as continual and\, feder
 ated learning along with learning from demonstration\, to help robots acqu
 ire complex skills and adapt over time. We will also discuss how sensor da
 ta can improve human-robot understanding\, particularly through multimodal
  fusion for intention prediction\, shared control\, and assessing trust in
  the robot. Real-world applications\, including robotic wheelchairs\, will
  be used to illustrate the challenges and benefits of user-centred learnin
 g for assistive robotics.\n\nAbout the Series:\nThis lecture is part of th
 e IEEE UK &amp; Ireland RAS Chapter Early-Career Researcher Seminar Series\, w
 hich highlights promising young researchers in robotics and automation.\n\
 nOrganisers:\nIEEE UK &amp; Ireland RAS Chapter\n\nRegistration\nIEEE and non-
 IEEE Members are invited to register and participate.\n\nAgenda: \nDate: 1
  October 2025\nTime: 11:00 – 12:15 (London Time)\n\n-\n11:00 – 11:05 |
  Welcome &amp; Introduction – IEEE UK &amp; Ireland RAS Chapter\n\n-\n11:05 – 
 11:50 | User-Centred Machine Learning for Assistive Robotics\nSpeaker: Dr.
  Fernando E. Casado\, Imperial College London\n\n-\n11:50 – 12:15 | Live
  Q&amp;A Session with the Speaker and Closing Remarks &amp; Upcoming Events\n\nVir
 tual: https://events.vtools.ieee.org/m/499982
LOCATION:Virtual: https://events.vtools.ieee.org/m/499982
ORGANIZER:figueredo@ieee.org
SEQUENCE:21
SUMMARY:IEEE UK &amp; Ireland RAS Chapter – ECR Lecturer-Series – User-Cent
 red Machine Learning for Assistive Robotics\, Dr. Fernando E. Casado
URL;VALUE=URI:https://events.vtools.ieee.org/m/499982
X-ALT-DESC:Description: &lt;br /&gt;&lt;p dir=&quot;ltr&quot; style=&quot;text-align: center\;&quot;&gt;&lt;st
 rong&gt;IEEE UK and Ireland &amp;ndash\; RAS Chapter &lt;/strong&gt;&lt;br&gt;&lt;strong&gt;Early-C
 areer Researcher Seminar&lt;/strong&gt;&lt;/p&gt;\n&lt;p data-start=&quot;823&quot; data-end=&quot;939&quot;&gt;
 &lt;a href=&quot;https://drive.google.com/file/d/1HZjkgMNmkGN5TDYdX5SAZk_Xt0r6sNHn
 /view?usp=sharing&quot;&gt;&lt;strong data-start=&quot;823&quot; data-end=&quot;835&quot;&gt;More informatio
 n: Link&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;\n&lt;p data-start=&quot;823&quot; data-end=&quot;939&quot;&gt;&lt;strong data-
 start=&quot;823&quot; data-end=&quot;835&quot;&gt;Speaker:&amp;nbsp\; &amp;nbsp\;&lt;/strong&gt;&lt;strong&gt;&lt;em dat
 a-start=&quot;838&quot; data-end=&quot;862&quot;&gt;Dr. F&lt;/em&gt;&lt;/strong&gt;&lt;em data-start=&quot;838&quot; data-
 end=&quot;862&quot;&gt;&lt;strong id=&quot;docs-internal-guid-14e7a502-7fff-7989-c357-a58979a50
 19c&quot;&gt;&lt;em data-start=&quot;838&quot; data-end=&quot;862&quot;&gt;ernand&lt;/em&gt;&lt;em data-start=&quot;838&quot; d
 ata-end=&quot;862&quot;&gt;&lt;strong id=&quot;docs-internal-guid-14e7a502-7fff-7989-c357-a5897
 9a5019c&quot;&gt;&lt;em data-start=&quot;838&quot; data-end=&quot;862&quot;&gt;o E. Casado&lt;/em&gt;&lt;/strong&gt;&lt;/em
 &gt;&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;\n&lt;p data-start=&quot;823&quot; data-end=&quot;939&quot;&gt;Research Associate
 \, Personal Robotics Lab (PRL)\, Imperial College London&lt;/p&gt;\n&lt;p data-star
 t=&quot;941&quot; data-end=&quot;1399&quot;&gt;Fernando is a Research Associate at the Personal R
 obotics Lab\, Imperial College London\, UK. He received his B.Sc.\, M.Sc.\
 , and Ph.D. in Computer Science and Artificial Intelligence from the Unive
 rsity of Santiago de Compostela\, Spain\, between 2017 and 2022. His resea
 rch interests focus on privacy-aware\, multi-robot and multi-user machine 
 learning for modelling\, adaptation\, and personalisation of robotic behav
 iours in trustworthy &amp;nbsp\;human&amp;ndash\;robot interaction.&lt;/p&gt;\n&lt;p data-s
 tart=&quot;1401&quot; data-end=&quot;2103&quot;&gt;&lt;strong data-start=&quot;1401&quot; data-end=&quot;1414&quot;&gt;Abst
 ract:&lt;/strong&gt;&lt;br data-start=&quot;1414&quot; data-end=&quot;1417&quot;&gt;Assistive robots can g
 reatly enhance the &amp;nbsp\;independence and quality of life of those most i
 n need. However\, &amp;nbsp\;developing intelligent robotic systems that learn
  to help and adapt &amp;nbsp\;to users remains challenging. Unlike other AI ap
 plications\, assistive robotics faces data scarcity due to the high cost o
 f real-world data collection and the need for human-in-the-loop learning a
 nd personalisation. As personal robots are deployed in homes\, vast amount
 s of sensitive interaction data (such as gaze\, facial expressions\, and e
 nvironmental context) will become available. Effectively leveraging this d
 ata requires machine learning strategies that integrate the user while pro
 tecting their privacy. In this seminar\, we will explore techniques such a
 s continual and\, federated learning along with learning from demonstratio
 n\, to help robots acquire complex skills and adapt over time. We will als
 o discuss how sensor data can improve human-robot understanding\, particul
 arly through multimodal fusion for intention prediction\, shared control\,
  and assessing trust in the robot. Real-world applications\, including rob
 otic wheelchairs\, will be used to illustrate the challenges and benefits 
 of user-centred learning for assistive robotics.&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p data-star
 t=&quot;2643&quot; data-end=&quot;2841&quot;&gt;&lt;strong data-start=&quot;2643&quot; data-end=&quot;2664&quot;&gt;About t
 he Series:&lt;/strong&gt;&lt;br data-start=&quot;2664&quot; data-end=&quot;2667&quot;&gt;This lecture is p
 art of the IEEE UK &amp;amp\; Ireland RAS Chapter &lt;em data-start=&quot;2725&quot; data-e
 nd=&quot;2765&quot;&gt;Early-Career Researcher Seminar Series&lt;/em&gt;\, which highlights p
 romising young researchers in robotics and automation.&lt;/p&gt;\n&lt;p data-start=
 &quot;2843&quot; data-end=&quot;2892&quot;&gt;&lt;strong data-start=&quot;2843&quot; data-end=&quot;2858&quot;&gt;Organiser
 s:&lt;/strong&gt;&lt;br data-start=&quot;2858&quot; data-end=&quot;2861&quot;&gt;IEEE UK &amp;amp\; Ireland RA
 S Chapter&lt;/p&gt;\n&lt;p data-start=&quot;2894&quot; data-end=&quot;2981&quot;&gt;&lt;strong data-start=&quot;28
 94&quot; data-end=&quot;2910&quot;&gt;Registration&lt;/strong&gt;&lt;br data-start=&quot;2910&quot; data-end=&quot;2
 913&quot;&gt;IEEE and non-IEEE Members are invited to register and participate.&lt;/p
 &gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p data-start=&quot;172&quot; data-end=&quot;230&quot;&gt;&lt;strong data
 -start=&quot;172&quot; data-end=&quot;181&quot;&gt;Date:&lt;/strong&gt; 1 October 2025&lt;br data-start=&quot;1
 96&quot; data-end=&quot;199&quot;&gt;&lt;strong data-start=&quot;199&quot; data-end=&quot;208&quot;&gt;Time:&lt;/strong&gt; 
 11:00 &amp;ndash\; 12:15 (London Time)&lt;/p&gt;\n&lt;ul data-start=&quot;232&quot; data-end=&quot;571
 &quot;&gt;\n&lt;li data-start=&quot;232&quot; data-end=&quot;310&quot;&gt;\n&lt;p data-start=&quot;234&quot; data-end=&quot;31
 0&quot;&gt;&lt;strong data-start=&quot;234&quot; data-end=&quot;251&quot;&gt;11:00 &amp;ndash\; 11:05&lt;/strong&gt; |
  Welcome &amp;amp\; Introduction &amp;ndash\; IEEE UK &amp;amp\; Ireland RAS Chapter&lt;/
 p&gt;\n&lt;/li&gt;\n&lt;li data-start=&quot;311&quot; data-end=&quot;455&quot;&gt;\n&lt;p data-start=&quot;313&quot; data-
 end=&quot;455&quot;&gt;&lt;strong data-start=&quot;313&quot; data-end=&quot;330&quot;&gt;11:05 &amp;ndash\; 11:50&lt;/st
 rong&gt; | &lt;em data-start=&quot;333&quot; data-end=&quot;387&quot;&gt;User-Centred Machine Learning 
 for Assistive Robotics&lt;/em&gt;&lt;br data-start=&quot;387&quot; data-end=&quot;390&quot;&gt;&lt;strong dat
 a-start=&quot;393&quot; data-end=&quot;405&quot;&gt;Speaker:&lt;/strong&gt; Dr. Fernando E. Casado\, Im
 perial College London&lt;/p&gt;\n&lt;/li&gt;\n&lt;li data-start=&quot;456&quot; data-end=&quot;513&quot;&gt;\n&lt;p
  data-start=&quot;458&quot; data-end=&quot;513&quot;&gt;&lt;strong data-start=&quot;458&quot; data-end=&quot;475&quot;&gt;1
 1:50 &amp;ndash\; 12:15&lt;/strong&gt; | Live Q&amp;amp\;A Session with the Speaker and&amp;
 nbsp\;Closing Remarks &amp;amp\; Upcoming Events&lt;/p&gt;\n&lt;/li&gt;\n&lt;/ul&gt;
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