1 code implementation • 29 Dec 2020 • Vikas Dhiman, Mohammad Javad Khojasteh, Massimo Franceschetti, Nikolay Atanasov
This paper focuses on learning a model of system dynamics online while satisfying safety constraints.
no code implementations • 21 Nov 2020 • Anshuka Rangi, Mohammad Javad Khojasteh, Massimo Franceschetti
We study the trade-offs between the information acquired by the attacker from observations, the detection capabilities of the controller, and the control cost.
1 code implementation • 11 Apr 2020 • Richard Cheng, Mohammad Javad Khojasteh, Aaron D. Ames, Joel W. Burdick
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles.
1 code implementation • L4DC 2020 • Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov
This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics.
no code implementations • 17 Sep 2018 • Mohammad Javad Khojasteh, Anatoly Khina, Massimo Franceschetti, Tara Javidi
In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics.
no code implementations • 1 Apr 2018 • Mohammad Javad Khojasteh, Massimo Franceschetti, Gireeja Ranade
Each symbol transmitted from a sensor to a controller in a closed-loop system is received subject to some to random delay.