Learning to Plan over Symbolic World Models for Embodied AI via Goal Regression

💬 Discord: https://discord.gg/h8NVzwnysW 💻 GitHub: https://github.com/centaurinstitute 🤝 LinkedIn: https://www.linkedin.com/company/centaur-ai-institute/ 📢 New Initiative: Neuro-Symbolic Agentic Protocol ⭐ Star us on GitHub https://github.com/centaurinstitute/neuro-symbolic-agentic-protocol — ​​​​​​👉 Abstract: We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems with the aid of symbolic world models. We leverage and revitalize a foundational symbolic technique--namely goal regression--to learn powerful policies for long-horizon planning and to guide embodied agents. ​📎 Links to Papers: - https://arxiv.org/abs/2511.11095 - https://arxiv.org/abs/2605.15975 ​🔬 Bio: Dillon Chen is a PhD student at the University of Toronto. His research interests include learning and planning with world models. — 🗨️ ​Join us for an interactive session exploring Neuro-Symbolic AI, the emerging paradigm that blends the strengths of neural networks with symbolic reasoning. We will discuss how hybrid approaches can enhance generalization, interpretability, and reasoning, and how these methods are shaping the future of intelligent systems. Whether you’re a researcher, engineer, or simply curious about the cutting edge of AI, you’ll find an engaging space to learn, connect, and exchange ideas. Event Format: 🔍 Reading Group 🎙️ Panel Discussion 🧩 Tutorials/Workshop Series 💬 Research Roundtable 🛠️ Open-Source Project Review 📄 Paper Pitch 🤝 Collaboration Hour 🍵 Networking Mixer #AI #NeuroSymbolic #FutureOfAI