Publications (22)
Certifiably-Correct Mapping for Safe Navigation Despite Odometry Drift
Devansh R. Agrawal, Taekyung Kim, Rajiv Govindjee +4
Accurate perception, state estimation and mapping are essential for safe robotic navigation as planners and controllers rely on these components for safety-critical decisions. Howe…
HugAgent: Benchmarking LLMs for Simulation of Individualized Human Reasoning
Chance Jiajie Li, Zhenze Mo, Yuhan Tang +11
Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now approximate human responses at scale,…
LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimization
Simon Paquette-Greenbaum, Jiangbo Yu
Investment portfolio optimization is a task conducted in all major financial institutions. The Cardinality Constrained Mean-Variance Portfolio Optimization (CCPO) problem formulati…
FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback
Kangan Qian, Ziang Luo, Sicong Jiang +16
Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems. While end-to-end models trained on large datasets perform well in standard driving sce…
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
Yuebing Liang, Shenhao Wang, Jiangbo Yu +3
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To a…
Online and Certifiably Correct Visual Odometry and Mapping
Devansh R Agrawal, Rajiv Govindjee, Jiangbo Yu +2
This paper proposes two new algorithms for certified perception in safety-critical robotic applications. The first is a Certified Visual Odometry algorithm, which uses a RGBD camer…