138 citations · 402 across the 159 of their papers we have counts for
33 papers · 1 filter
Learning to Trust: Bayesian Adaptation to Varying Suggester Reliability in Sequential Decision Making
Dylan M. Asmar, Mykel J. Kochenderfer
Autonomous agents operating in sequential decision-making tasks under uncertainty can benefit from external action suggestions, which provide valuable guidance but inherently vary…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
SCOUT: A Lightweight Framework for Scenario Coverage Assessment in Autonomous Driving
Anil Yildiz, Sarah M. Thornton, Carl Hildebrandt +2
Assessing scenario coverage is crucial for evaluating the robustness of autonomous agents, yet existing methods rely on expensive human annotations or computationally intensive Lar…
Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance
Sydney M. Katz, Robert J. Moss, Dylan M. Asmar +3
Aircraft collision avoidance systems is critical to modern aviation. These systems are designed to predict potential collisions between aircraft and recommend appropriate avoidance…
Scalable Ground Station Selection for Large LEO Constellations
Grace Ra Kim, Duncan Eddy, Vedant Srinivas +1
Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce commun…
An Adaptive Responsible AI Governance Framework for Decentralized Organizations
Kiana Jafari Meimandi, Anka Reuel, Gabriela Aranguiz-Dias +6
This paper examines the assessment challenges of Responsible AI (RAI) governance efforts in globally decentralized organizations through a case study collaboration between a leadin…
Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Grace Ra Kim, Hailey Warner, Duncan Eddy +4
Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communicat…
Resource Allocation under Stochastic Demands using Shrinking Horizon Optimization
Alexandros E. Tzikas, Nazim Kemal Ure, Mansur Arief +2
We consider the problem of optimally allocating a limited number of resources across time to maximize revenue under stochastic demands. This formulation is relevant in various area…
Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers
Isaac Ronald Ward, Mark Paral, Kristopher Riordan +1
Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue.…
Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System
Romeo Valentin, Sydney M. Katz, Artur B. Carneiro +2
Recent advances in data-driven computer vision have enabled robust autonomous navigation capabilities for civil aviation, including automated landing and runway detection. However,…
Distributionally Robust Control with Constraints on Linear Unidimensional Projections
Alexandros E. Tzikas, Lukas Fiechtner, Arec Jamgochian +1
Distributionally robust control is a well-studied framework for optimal decision making under uncertainty, with the objective of minimizing an expected cost function over control a…
Adaptive Strategies for Pension Fund Management
Raphael Chinchilla, Thomas D. Rueter, Timothy R. McDade +4
This paper proposes a simulation-based framework for assessing and improving the performance of a pension fund management scheme. This framework is modular and allows the definitio…
Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer +1
Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions with statistically valid coverage guarantees. Howev…
Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers
Juanran Wang, Marc R. Schlichting, Mykel J. Kochenderfer
High-risk traffic zones such as intersections are a major cause of collisions. This study leverages deep generative models to enhance the safety of autonomous vehicles in an inters…
Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
Muhammad Fadhil Ginting, Dong-Ki Kim, Xiangyun Meng +10
As robots become increasingly capable of operating over extended periods -- spanning days, weeks, and even months -- they are expected to accumulate knowledge of their environments…
Digital Simulations to Enhance Military Medical Evacuation Decision-Making
Jeremy Fischer, Mahdi Al-Husseini, Ram Krishnamoorthy +2
Medical evacuation is one of the United States Army's most storied and critical mission sets, responsible for efficiently and expediently evacuating the battlefield ill and injured…
Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki +1
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential fail…
General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting
Bernard Lange, Anil Yildiz, Mansur Arief +3
Developing general-purpose navigation policies for unknown environments remains a core challenge in robotics. Most existing systems rely on task-specific neural networks and fixed…
The Measurement Imbalance in Agentic AI Evaluation Undermines Industry Productivity Claims
Kiana Jafari Meimandi, Gabriela Aránguiz-Dias, Grace Ra Kim +3
As industry reports claim agentic AI systems deliver double-digit productivity gains and multi-trillion dollar economic potential, the validity of these claims has become critical…
Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization
Francois Chaubard, Mykel Kochenderfer
During inference, Recurrent Neural Networks (RNNs) scale constant in both FLOPs and GPU memory with increasing context length, as they compress all prior tokens into a fixed-size m…
Model Identification Adaptive Control with -POMDP Planning
Michelle Ho, Arec Jamgochian, Mykel J. Kochenderfer
Accurate system modeling is crucial for safe, effective control, as misidentification can lead to accumulated errors, especially under partial observability. We address this proble…
Scalable Importance Sampling in High Dimensions with Low-Rank Mixture Proposals
Liam A. Kruse, Marc R. Schlichting, Mykel J. Kochenderfer
Importance sampling is a Monte Carlo technique for efficiently estimating the likelihood of rare events by biasing the sampling distribution towards the rare event of interest. By…
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…
On Technique Identification and Threat-Actor Attribution using LLMs and Embedding Models
Kyla Guru, Robert J. Moss, Mykel J. Kochenderfer
Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation c…