activity
20162026
most citedCieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

3 citations · 5 across the 9 of their papers we have counts for

collaborators

17 papers

cs.RO2026

Continuous-Time Gaussian Belief Trees for Motion Planning

Rayan Mazouz, Qi Heng Ho, Zachary N. Sunberg +1

We address sampling-based motion planning for continuous-time stochastic systems under process and measurement uncertainty, with probabilistic guarantees on safety and performance.…

cs.AI2026

Robustness Analysis of POMDP Policies to Observation Perturbations

Benjamin Kraske, Qi Heng Ho, Federico Rossi +2

Policies for Partially Observable Markov Decision Processes (POMDPs) are often designed using a nominal system model. In practice, this model can deviate from the true system durin…

cs.AI2026

Leveraging the Value of Information in POMDP Planning

Zakariya Laouar, Qi Heng Ho, Zachary Sunberg

Partially observable Markov decision processes (POMDPs) offer a principled formalism for planning under state and transition uncertainty. Despite advances made towards solving larg…

cs.RO2026

Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian

This paper tackles the problem of integrated task and kinodynamic motion planning in uncertain environments. We consider a robot with nonlinear dynamics tasked with a Linear Tempor…

cs.AI2024

Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives

Qi Heng Ho, Martin S. Feather, Federico Rossi +2

Partially Observable Markov Decision Processes (POMDPs) are powerful models for sequential decision making under transition and observation uncertainties. This paper studies the ch…

cs.AI2024

Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies

Benjamin Kraske, Zakariya Laouar, Zachary Sunberg

As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aim…