9 papers
Embodied Task Planning via Graph-Informed Action Generation with Large Language Models
Xiang Li, Ning Yan, Masood Mortazavi
While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon…
PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning
Zihan Zhao, Kaushik Pendiyala, Masood Mortazavi +1
Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor-asymmetry problem: the richest modali…
EvoMem: Improving Multi-Agent Planning with Dual-Evolving Memory
Wenzhe Fan, Ning Yan, Masood Mortazavi
Planning has been a cornerstone of artificial intelligence for solving complex problems, and recent progress in LLM-based multi-agent frameworks have begun to extend this capabilit…
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
Amit Roy, Ning Yan, Masood Mortazavi
Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes an…
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Yihong Ma, Ning Yan, Jiayu Li +2
Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification an…
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
Mi Zhou, Jiazhi Li, Masood Mortazavi +2
In this article, a \underline{S}tate-dependent \underline{M}ulti-\underline{A}gent \underline{D}eep \underline{D}eterministic \underline{P}olicy \underline{G}radient (\textbf{SMADD…