Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Vlahov, Bogdan"'
This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control, Tube-Model Predictive Path Integral Control, and Robust Model Predict
Externí odkaz:
http://arxiv.org/abs/2409.07563
Autor:
Vlahov, Bogdan, Gibson, Jason, Fan, David D., Spieler, Patrick, Agha-mohammadi, Ali-akbar, Theodorou, Evangelos A.
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 5, pp.4543-4550, 2024
Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be im
Externí odkaz:
http://arxiv.org/abs/2404.03094
Autor:
Gibson, Jason, Vlahov, Bogdan, Fan, David, Spieler, Patrick, Pastor, Daniel, Agha-mohammadi, Ali-akbar, Theodorou, Evangelos A.
Modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modeling problems. An off-road vehicle encounters highly complex and di
Externí odkaz:
http://arxiv.org/abs/2305.02241
In this paper, we present a new trajectory optimization algorithm for stochastic linear systems which combines Model Predictive Path Integral (MPPI) control with Constrained Covariance Steering (CSS) to achieve high performance with safety guarantees
Externí odkaz:
http://arxiv.org/abs/2110.07744
Autor:
Wang, Ziyi, So, Oswin, Gibson, Jason, Vlahov, Bogdan, Gandhi, Manan S., Liu, Guan-Horng, Theodorou, Evangelos A.
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimality likelihood function, a novel T
Externí odkaz:
http://arxiv.org/abs/2104.00241
In this paper we propose a novel decision making architecture for Robust Model Predictive Path Integral control (RMPPI) and investigate its performance guarantees and applicability to off-road navigation. Key building blocks of the proposed architect
Externí odkaz:
http://arxiv.org/abs/2102.09027
In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Net
Externí odkaz:
http://arxiv.org/abs/2004.08051
This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of fa
Externí odkaz:
http://arxiv.org/abs/1811.12555
Autor:
Zafar, Munzir, Patel, Akash, Vlahov, Bogdan, Glaser, Nathaniel, Aguillera, Sergio, Hutchinson, Seth
We present a novel application of robust control and online learning for the balancing of a n Degree of Freedom (DoF), Wheeled Inverted Pendulum (WIP) humanoid robot. Our technique condenses the inaccuracies of a mass model into a Center of Mass (CoM
Externí odkaz:
http://arxiv.org/abs/1810.03076
Publikováno v:
2022 American Control Conference (ACC).
In this paper, we present a new trajectory optimization algorithm for stochastic linear systems which combines Model Predictive Path Integral (MPPI) control with Constrained Covariance Steering (CSS) to achieve high performance with safety guarantees