76 citations · 308 across the 72 of their papers we have counts for
8 papers · 1 filter
Plane Wave Elastography: A Frequency-Domain Ultrasound Shear Wave Elastography Approach
Reza Khodayi-mehr, Matthew W. Urban, Michael M. Zavlanos +1
In this paper, we propose Plane Wave Elastography (PWE), a novel ultrasound shear wave elastography (SWE) approach. Currently, commercial methods for SWE rely on directional filter…
Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback
Yijie Zhou, Yan Zhang, Xusheng Luo +1
In this paper, we consider a robot navigation problem in environments populated by humans. The goal is to determine collision-free and dynamically feasible trajectories that also m…
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
Alper Kamil Bozkurt, Yu Wang, Michael Zavlanos +1
We study the problem of synthesizing control strategies for Linear Temporal Logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochasti…
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
Yan Zhang, Yi Zhou, Kaiyi Ji +1
Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the unknown gradient of the objective function. Nevertheless, two-point feedback can not be used f…
Cooperative Multi-Agent Reinforcement Learning with Partial Observations
Yan Zhang, Michael M. Zavlanos
In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agen…
A New One-Point Residual-Feedback Oracle For Black-Box Learning and Control
Yan Zhang, Yi Zhou, Kaiyi Ji +1
Zeroth-order optimization (ZO) algorithms have been recently used to solve black-box or simulation-based learning and control problems, where the gradient of the objective function…
Transfer Reinforcement Learning under Unobserved Contextual Information
Yan Zhang, Michael M. Zavlanos
In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a dem…
Socially-Aware Robot Planning via Bandit Human Feedback
Xusheng Luo, Yan Zhang, Michael M. Zavlanos
In this paper, we consider the problem of designing collision-free, dynamically feasible, and socially-aware trajectories for robots operating in environments populated by humans.…