<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Control | Henry Kou</title><link>https://kenryhou2.github.io/tags/neural-control/</link><atom:link href="https://kenryhou2.github.io/tags/neural-control/index.xml" rel="self" type="application/rss+xml"/><description>Neural Control</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 07 May 2026 00:00:00 +0000</lastBuildDate><image><url>https://kenryhou2.github.io/media/icon_hu_da05098ef60dc2e7.png</url><title>Neural Control</title><link>https://kenryhou2.github.io/tags/neural-control/</link></image><item><title>EigenBot</title><link>https://kenryhou2.github.io/projects/eigenbot/</link><pubDate>Thu, 07 May 2026 00:00:00 +0000</pubDate><guid>https://kenryhou2.github.io/projects/eigenbot/</guid><description>&lt;p&gt;I use EigenBot as a way to study modular limbs where the hardware, sensing, and controller are all tightly coupled. A modular robot is appealing because the same building blocks can become many morphologies, but that flexibility makes control harder: the controller has to reason about contact, compliance, and changing body geometry.&lt;/p&gt;
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&lt;img alt="EigenBot full limb platform"
srcset="https://kenryhou2.github.io/projects/eigenbot/eigenbot_full_limb_hu_6a5e232051268d94.webp 320w, https://kenryhou2.github.io/projects/eigenbot/eigenbot_full_limb_hu_b39c3640f486c383.webp 480w, https://kenryhou2.github.io/projects/eigenbot/eigenbot_full_limb_hu_3cbe25c28ba96f9e.webp 760w"
sizes="(max-width: 480px) 100vw, (max-width: 768px) 90vw, (max-width: 1024px) 80vw, 760px"
src="https://kenryhou2.github.io/projects/eigenbot/eigenbot_full_limb_hu_6a5e232051268d94.webp"
width="760"
height="507"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
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&lt;p&gt;My part of the work focuses on force-sensing experiments, full-limb behavior, and early neural-controller results. The full-limb platform gives a concrete test case for asking whether local sensing can support useful global motion, especially when the robot is assembled from repeated modules rather than a single monolithic mechanism. See the
and watch the
.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Sources I leaned on:&lt;/strong&gt; Yim, Shen, Salemi, Rus, Moll, Lipson, Klavins, and Chirikjian&amp;rsquo;s modular self-reconfigurable robot survey; Cheney, Bongard, Lipson, and Clune&amp;rsquo;s evolved soft robot work for morphology-control coupling; and recent differentiable or neural locomotion papers as context for data-driven controllers on physical robots.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; EigenBot, modular robotics, force sensing, robot limbs, neural control, embedded sensing, physical robot experiments.&lt;/p&gt;</description></item><item><title>Bio-Inspired Distributed Neural Locomotion Controller (D-NLC) for Robust Locomotion and Emergent Behaviors</title><link>https://kenryhou2.github.io/publications/dnlc-robust-locomotion/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://kenryhou2.github.io/publications/dnlc-robust-locomotion/</guid><description>&lt;p&gt;Publication entry sourced from the resume. This work is associated with the EigenBot distributed neural locomotion controller project.&lt;/p&gt;</description></item></channel></rss>