1 citations · 1 across the 3 of their papers we have counts for
4 papers
Policy Gradient over History-Dependent Policy Classes for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Anastasios Tsiamis +2
Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. Whil…
Policy Gradient for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
Domain randomization (DR) enables sim-to-real transfer by training controllers on a distribution of simulated environments, with the goal of achieving robust performance in the rea…
Domain Randomization is Sample Efficient for Linear Quadratic Control
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
We study the sample efficiency of domain randomization and robust control for the benchmark problem of learning the linear quadratic regulator (LQR). Domain randomization, which sy…
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks
Makoto Saito, Junya Yamauchi, Tesshu Fujinami +2
In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consid…