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DTSTART;TZID=America/New_York:20261111T130000
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UID:2765-1794402000-1794405600@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:The next meeting of our working group will be tomorrow\, Wed Nov 11 at 1pm on Zoom and in person at Schermerhorn 406 at Columbia. We will feature a presentation by Hussein Zolfaghari who will talk about Continual Learning for Personalized Assistive Control: An OpenSim Exosuit Case Study. The abstract is detailed below.\n\nContinual Learning for Personalized Assistive Control: An OpenSim Exosuit Case Study\n \nMany individuals experience reduced muscle strength due to injury\, aging\, or other physical conditions\, motivating assistive technologies that can adapt to users with different and changing physical capabilities. Such changes also provide a natural continual learning problem: an intelligent system must adapt to new user conditions while retaining knowledge acquired under previous conditions. This study investigates a neural network controller with replay-based continual learning for personalized elbow assistance under changing muscle-strength conditions. A cable-driven elbow exosuit was simulated using an OpenSim upper-limb model with a 5-kg payload. The neural controller was trained sequentially to generate assistive cable forces for elbow trajectory tracking under four simulated muscle-strength levels corresponding to 90%\, 60%\, 45%\, and 30% of healthy reference capacity. The controller was subsequently evaluated on previously learned conditions to quantify catastrophic forgetting. Standard sequential training showed substantial degradation on earlier conditions\, whereas replay-based continual learning largely preserved previously learned control performance. Across three selected validation comparisons\, continual learning reduced the mean elbow-angle tracking RMSE from 3.81° to 0.048°\, corresponding to a 98.8% reduction. Beyond this specific control application\, physics-based musculoskeletal simulation offers a rich environment for studying continual learning under naturally evolving human conditions. OpenSim can provide controlled changes in muscle capacity\, fatigue\, movement\, payload\, impairment\, and user characteristics\, creating opportunities to systematically study adaptation\, forgetting\, transfer\, and benchmarking of continual-learning algorithms in human-centered intelligent systems.
URL:https://arni-institute.org/event/arni-continual-learning-working-group-7/
LOCATION:Schermerhorn 406
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DTSTART;TZID=America/New_York:20261113T113000
DTEND;TZID=America/New_York:20261113T133000
DTSTAMP:20261111T173947Z
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UID:2759-1794569400-1794576600@arni-institute.org
SUMMARY:CTN Speaker: Bing Wen Brunton
DESCRIPTION:Bing Wen Brunton\n\n\n\n\n\nSeminar Time: 11:30am\n\nDate: Fri 11/13/26\nSeminar Location: JLG\, L5-084 \n\n\nHost: Chase King\n\n \n\nTitle: Embodied intelligence: From connectomes to body models \nAbstract: Brains and nervous systems evolved jointly with the bodies they inhabit. To understand brains\, behavior\, and intelligence\, we must embrace the embodied nature of neural computation. In this talk\, I will describe some ongoing collaborative work towards putting dynamic models of brains as controllers inside the bodies they evolved to sense and actuate. I will present our recent work developing connectome simulations of the Drosophila ventral nerve cord (VNC)\, where we identified a three-neuron circuit as a putative central pattern generator (CPG) for fly walking. I suggest that integrating simulations of connectomes with physically realistic body models will allow us to investigate active behavioral sequences and how specific recurrent neural circuits support robust\, embodied intelligence in animals.
URL:https://arni-institute.org/event/2759/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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DTSTART;TZID=America/New_York:20261120T083000
DTEND;TZID=America/New_York:20261120T170000
DTSTAMP:20261027T172648Z
CREATED:20261027T172611Z
LAST-MODIFIED:20261027T172648Z
UID:2742-1795163400-1795194000@arni-institute.org
SUMMARY:Continual Learning in Minds and Machines Workshop
DESCRIPTION:Overview \nWebpage\n\n\n\nHow can AI systems keep learning from experience? \n\n\n\n\nAn assistant working with a person or organization over time must remember past interactions\, learn from limited feedback\, and adapt as circumstances change. Continual learning studies how systems acquire new knowledge while preserving what remains useful. These questions connect AI research with the study of human learning and memory. \nCo-organized by Columbia Business School’s AI in Business Initiative and the NSF AI Institute for Artificial and Natural Intelligence (ARNI)\, this workshop brings together researchers in machine learning\, neuroscience\, and cognitive science with industry teams building continually learning systems. We will examine how minds and machines learn over time\, what biological memory can teach us about AI\, and how to measure whether accumulated experience improves an agent’s decisions. \nPlease note that this event is intended for researchers in the field\, not for Master’s or MBA students. A promotional code is required for registration. Please contact ARNI@columbia.edu if you are interested in registering for this workshop. 
URL:https://arni-institute.org/event/continual-learning-in-minds-and-machines-workshop/
LOCATION:Columbia Business School\, David Geffen Hall
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
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