26 citations · 94 across the 25 of their papers we have counts for
5 papers · 1 filter
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
Thomas Lew, Apoorva Sharma, James Harrison +2
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation. Specifically, we disc…
Bayesian Embeddings for Few-Shot Open World Recognition
John Willes, James Harrison, Ali Harakeh +3
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and…
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic…
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
Rohan Sinha, James Harrison, Spencer M. Richards +1
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally…
Particle MPC for Uncertain and Learning-Based Control
Robert Dyro, James Harrison, Apoorva Sharma +1
As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essent…