113 citations · 167 across the 25 of their papers we have counts for
27 papers
Conditional Deep Generative Models for Belief State Planning
Antoine Bigeard, Anthony Corso, Mykel Kochenderfer
Partially observable Markov decision processes (POMDPs) are used to model a wide range of applications, including robotics, autonomous vehicles, and subsurface problems. However, a…
Managing Geological Uncertainty in Critical Mineral Supply Chains: A POMDP Approach with Application to U.S. Lithium Resources
Mansur Arief, Yasmine Alonso, CJ Oshiro +5
The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition pr…
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems
Harrison Delecki, Marc R. Schlichting, Mansur Arief +3
Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge. Existing black-box approaches based on Markov chain Mon…
ConstrainedZero: Chance-Constrained POMDP Planning using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints
Robert J. Moss, Arec Jamgochian, Johannes Fischer +2
To plan safely in uncertain environments, agents must balance utility with safety constraints. Safe planning problems can be modeled as a chance-constrained partially observable Ma…
Constrained Hierarchical Monte Carlo Belief-State Planning
Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray +2
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning…
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning
Marc R. Schlichting, Nina V. Boord, Anthony L. Corso +1
Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the co…