37 citations · 151 across the 85 of their papers we have counts for
18 papers · 1 filter
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
Gokul Puthumanaillam, Jae Hyuk Song, Nurzhan Yesmagambet +2
Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinati…
Resolvent-Type Data-Driven Learning of Generators for Unknown Continuous-Time Dynamical Systems
Yiming Meng, Ruikun Zhou, Melkior Ornik +1
A semigroup characterization, or equivalently, a characterization by the generator, is a classical technique used to describe continuous-time nonlinear dynamical systems. In the re…
Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach
Manav Vora, Ilan Shomorony, Melkior Ornik
We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a…
GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +2
Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth opera…
Energetic Resilience of Linear Driftless Systems
Ram Padmanabhan, Melkior Ornik
When a malfunction causes a control system to lose authority over a subset of its actuators, achieving a task may require spending additional energy in order to compensate for the…
Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control
Noah Franceschini, Pranay Thangeda, Melkior Ornik +1
This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a sta…
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management
Pranay Thangeda, Trevor S. Betz, Michael N. Grussing +1
Efficient management of infrastructure systems is crucial for economic stability, sustainability, and public safety. However, infrastructure sustainment is challenging due to the v…
How Much Reserve Fuel: Quantifying the Maximal Energy Cost of System Disturbances
Ram Padmanabhan, Craig Bakker, Siddharth Abhijit Dinkar +1
Motivated by the design question of additional fuel needed to complete a task in an uncertain environment, this paper introduces metrics to quantify the maximal additional energy u…
Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots
Arun N. Sivakumar, Pranay Thangeda, Yixiao Fang +4
Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception,…
Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps
Yifan Zhu, Pranay Thangeda, Erica L Tevere +5
Autonomous lander missions on extraterrestrial bodies need to sample granular materials while coping with domain shifts, even when sampling strategies are extensively tuned on Eart…
Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning
Manav Vora, Jonas Liang, Michael N. Grussing +1
Monotonic Partially Observable Markov Decision Processes (POMDPs), where the system state progressively decreases until a restorative action is performed, can be used to model sequ…
Optimizing Agricultural Order Fulfillment Systems: A Hybrid Tree Search Approach
Pranay Thangeda, Hoda Helmi, Melkior Ornik
Efficient order fulfillment is vital in the agricultural industry, particularly due to the seasonal nature of seed supply chains. This paper addresses the challenge of optimizing s…
Guaranteed Reachability on Riemannian Manifolds for Unknown Nonlinear Systems
Taha Shafa, Melkior Ornik
Determining the reachable set for a given nonlinear system is critically important for autonomous trajectory planning for reach-avoid applications and safety critical scenarios. Pr…
A Moral Imperative: The Need for Continual Superalignment of Large Language Models
Gokul Puthumanaillam, Manav Vora, Pranay Thangeda +1
This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical fram…
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular…
Koopman-Based Learning of Infinitesimal Generators without Operator Logarithm
Yiming Meng, Ruikun Zhou, Melkior Ornik +1
The Koopman operator has gained significant attention in recent years for its ability to verify evolutionary properties of continuous-time nonlinear systems by lifting state variab…
Online Learning and Control Synthesis for Reachable Paths of Unknown Nonlinear Systems
Yiming Meng, Taha Shafa, Jesse Wei +1
In this paper, we present a novel method to drive a nonlinear system to a desired state, with limited a priori knowledge of its dynamic model: local dynamics at a single point and…
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates
Gokul Puthumanaillam, Manav Vora, Melkior Ornik
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent o…