3 citations · 3 across the 16 of their papers we have counts for
19 papers
RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations
Dorian Benhamou Goldfajn, Mason Nakamura, Saaduddin Mahmud +3
Multi-robot collaboration could enable more efficient and scalable solutions to complex robotic tasks, but collaboration under partial observability remains challenging. Natural-la…
Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs
Jiho Lee, Nisar Ahmed, Kyle Hollins Wray +1
Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimensional state spaces. While sampl…
Multi-Robot Coordination for Planning under Context Uncertainty
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robot…
Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi…
Aligning LLMs on a Budget: Inference-Time Alignment with Heuristic Reward Models
Mason Nakamura, Saaduddin Mahmud, Kyle H. Wray +2
Aligning LLMs with user preferences is crucial for real-world use but often requires costly fine-tuning or expensive inference, forcing trade-offs between alignment quality and com…
Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models
Saaduddin Mahmud, Mason Nakamura, Kyle Hollins Wray +1
Prompt optimization methods have demonstrated significant effectiveness in aligning black-box large language models (LLMs). In parallel, inference scaling strategies such as Best-o…
Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms
Mahdi Al-Husseini, Kyle H. Wray, Mykel J. Kochenderfer
The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of…
Rao-Blackwellized POMDP Planning
Jiho Lee, Nisar R. Ahmed, Kyle H. Wray +1
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief update…
Watercraft as Overwater Ambulance Exchange Points to Enhance Aeromedical Evacuation
Mahdi Al-Husseini, Kyle H. Wray, Mykel J. Kochenderfer
Ambulance exchange points are preidentified sites where patients are transferred between evacuation platforms while en route to enhanced medical care. We propose a new capability f…
Hierarchical Framework for Optimizing Wildfire Surveillance and Suppression using Human-Autonomous Teaming
Mahdi Al-Husseini, Kyle Wray, Mykel Kochenderfer
The integration of manned and unmanned aircraft can help improve wildfire response. Wildfire containment failures occur when resources available to first responders, who execute th…
Entropy-regularized Point-based Value Iteration
Harrison Delecki, Marcell Vazquez-Chanlatte, Esen Yel +4
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based…
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…
Active teacher selection for reward learning
Rachel Freedman, Justin Svegliato, Kyle Wray +1
Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes f…
Experience Filter: Using Past Experiences on Unseen Tasks or Environments
Anil Yildiz, Esen Yel, Anthony L. Corso +3
One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very…
Multi-Objective Policy Gradients with Topological Constraints
Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer +1
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a c…
Competence-Aware Path Planning via Introspective Perception
Sadegh Rabiee, Connor Basich, Kyle Hollins Wray +2
Robots deployed in the real world over extended periods of time need to reason about unexpected failures, learn to predict them, and to proactively take actions to avoid future fai…
Improving Competence for Reliable Autonomy
Connor Basich, Justin Svegliato, Kyle Hollins Wray +2
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios…
Learning to Optimize Autonomy in Competence-Aware Systems
Connor Basich, Justin Svegliato, Kyle Hollins Wray +3
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows…
Planning in Stochastic Environments with Goal Uncertainty
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda +1
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncert…