<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Embodied AI | Zunzhe Zhang</title><link>https://tuagoale.github.io/tags/embodied-ai/</link><atom:link href="https://tuagoale.github.io/tags/embodied-ai/index.xml" rel="self" type="application/rss+xml"/><description>Embodied AI</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 08 Feb 2026 00:00:00 +0000</lastBuildDate><image><url>https://tuagoale.github.io/media/icon.svg</url><title>Embodied AI</title><link>https://tuagoale.github.io/tags/embodied-ai/</link></image><item><title>Virtual Community: An Open World for Humans, Robots, and Society</title><link>https://tuagoale.github.io/publications/virtual-community/</link><pubDate>Sun, 08 Feb 2026 00:00:00 +0000</pubDate><guid>https://tuagoale.github.io/publications/virtual-community/</guid><description>&lt;p&gt;Virtual Community studies what happens when humans and robots coexist in shared, physically grounded communities. The platform combines scalable scene generation, real-world geospatial structure, grounded agent communities, and robot simulation powered by a physics engine.&lt;/p&gt;
&lt;p&gt;The work introduces two benchmark settings: a Community Planning Challenge for open-world multi-agent reasoning, and a Community Robot Challenge for coordination among heterogeneous robots. Together, they make social and physical interaction testable at a scale that is difficult to study in isolated robotics benchmarks.&lt;/p&gt;</description></item><item><title>Ella: Embodied Social Agents with Lifelong Memory</title><link>https://tuagoale.github.io/publications/ella/</link><pubDate>Mon, 30 Jun 2025 00:00:00 +0000</pubDate><guid>https://tuagoale.github.io/publications/ella/</guid><description>&lt;p&gt;Ella focuses on long-horizon embodied social behavior: agents must observe, remember, plan, and interact across days rather than solve isolated tasks. Its memory system organizes acquired knowledge in semantic form while preserving episodic traces of multimodal experience.&lt;/p&gt;
&lt;p&gt;The system is evaluated in a dynamic 3D open world where multiple agents participate in social activities and are later tested on controlled tasks such as influence, leadership, and cooperation. The results show how structured memory can help foundation-model agents learn from observation and interaction.&lt;/p&gt;</description></item></channel></rss>