3 citations · 4 across the 6 of their papers we have counts for
7 papers
Strong Duality and Dual Ascent Approach to Continuous-Time Chance-Constrained Stochastic Optimal Control
Apurva Patil, Alfredo Duarte, Fabrizio Bisetti +1
The paper addresses a continuous-time continuous-space chance-constrained stochastic optimal control (SOC) problem where the probability of failure to satisfy given state constrain…
Model Predictive Path Integral Control for Roll-to-Roll Manufacturing
Christopher Martin, Apurva Patil, Wei Li +2
Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains…
Simulator-Driven Deceptive Control via Path Integral Approach
Apurva Patil, Mustafa O. Karabag, Takashi Tanaka +1
We consider a setting where a supervisor delegates an agent to perform a certain control task, while the agent is incentivized to deviate from the given policy to achieve its own g…
Risk-Minimizing Two-Player Zero-Sum Stochastic Differential Game via Path Integral Control
Apurva Patil, Yujing Zhou, David Fridovich-Keil +1
This paper addresses a continuous-time risk-minimizing two-player zero-sum stochastic differential game (SDG), in which each player aims to minimize its probability of failure. Fai…
Upper Bounds for Continuous-Time End-to-End Risks in Stochastic Robot Navigation
Apurva Patil, Takashi Tanaka
We present an analytical method to estimate the continuous-time collision probability of motion plans for autonomous agents with linear controlled Ito dynamics. Motion plans genera…
Chance-Constrained Stochastic Optimal Control via Path Integral and Finite Difference Methods
Apurva Patil, Alfredo Duarte, Aislinn Smith +2
This paper addresses a continuous-time continuous-space chance-constrained stochastic optimal control (SOC) problem via a Hamilton-Jacobi-Bellman (HJB) partial differential equatio…
Upper and Lower Bounds for End-to-End Risks in Stochastic Robot Navigation
Apurva Patil, Takashi Tanaka
We present novel upper and lower bounds to estimate the collision probability of motion plans for autonomous agents with discrete-time linear Gaussian dynamics. Motion plans genera…