41 citations · 105 across the 30 of their papers we have counts for
30 papers
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.…
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…
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…
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…
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…
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…
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
Matt-Heun Hong, Zachary N. Sunberg, Danielle Albers Szafir
Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significan…
Feasibility-Guided Safety-Aware Model Predictive Control for Jump Markov Linear Systems
Zakariya Laouar, Qi Heng Ho, Rayan Mazouz +2
In this paper, we present a controller framework that synthesizes control policies for Jump Markov Linear Systems subject to stochastic mode switches and imperfect mode estimation.…
Recursively-Constrained Partially Observable Markov Decision Processes
Qi Heng Ho, Tyler Becker, Benjamin Kraske +5
Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Process…
Human-Centered Autonomy for UAS Target Search
Hunter M. Ray, Zakariya Laouar, Zachary Sunberg +1
Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search \& rescue missions, require nearly continuous human s…
Explanation through Reward Model Reconciliation using POMDP Tree Search
Benjamin D. Kraske, Anshu Saksena, Anna L. Buczak +1
As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success. Ensurin…
Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS
Prashin Sharma, Benjamin Kraske, Joseph Kim +3
Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper pro…
Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems
Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints…
Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides
Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
This paper presents a new multi-layered algorithm for motion planning under motion and sensing uncertainties for Linear Temporal Logic specifications. We propose a technique to gui…
Optimality Guarantees for Particle Belief Approximation of POMDPs
Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer +2
Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to…
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
Qi Heng Ho, Roland B. Ilyes, Zachary N. Sunberg +1
This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications. We propose a novel algorit…
Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning
Himanshu Gupta, Bradley Hayes, Zachary Sunberg
This paper presents a hybrid online Partially Observable Markov Decision Process (POMDP) planning system that addresses the problem of autonomous navigation in the presence of mult…
Navigation between initial and desired community states using shortcuts
Benjamin W. Blonder, Michael H. Lim, Zachary Sunberg +1
Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation is possible without brute-force additions and deleti…
Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning
Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
In this paper, we address the problem of sampling-based motion planning under motion and measurement uncertainty with probabilistic guarantees. We generalize traditional sampling-b…
Compositional Learning-based Planning for Vision POMDPs
Sampada Deglurkar, Michael H. Lim, Johnathan Tucker +3
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty. However, most…
Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs
Michael H. Lim, Claire J. Tomlin, Zachary N. Sunberg
This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov d…
Bayesian Optimized Monte Carlo Planning
John Mern, Anil Yildiz, Zachary Sunberg +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening at…
APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance
Shakeeb Ahmad, Zachary N. Sunberg, J. Sean Humbert
This paper proposes a framework for 3D obstacle avoidance in the presence of partial observability of environment obstacles. The method focuses on the utility of the Artificial Pot…
Improving Automated Driving through POMDP Planning with Human Internal States
Zachary Sunberg, Mykel Kochenderfer
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and eff…