Výsledky vyhledávání - "Ojaghi, Pegah"

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Curriculum Is More Influential Than Haptic Information During Reinforcement Learning of Object Manipulation Against Gravity

Autor: Ojaghi, Pegah, Mir, Romina, Marjaninejad, Ali, Erwin, Andrew, Wehner, Michael, Valero-Cueva, Francisco J

Learning to lift and rotate objects with the fingertips is necessary for autonomous in-hand dexterous manipulation. In our study, we explore the impact of various factors on successful learning strategies for this task. Specifically, we investigate t

Externí odkaz: http://arxiv.org/abs/2407.09986

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Stochastic Uncertainty Propagation in Power System Dynamics using Measure-valued Proximal Recursions

Autor: Halder, Abhishek, Caluya, Kenneth F., Ojaghi, Pegah, Geng, Xinbo

We present a proximal algorithm that performs a variational recursion on the space of joint probability measures to propagate the stochastic uncertainties in power system dynamics over high dimensional state space. The proposed algorithm takes advant

Externí odkaz: http://arxiv.org/abs/2108.13405

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Akademický článek

A Model Predictive Control Framework for Hybrid Dynamical Systems

Autor: Altın, Berk, Ojaghi, Pegah, Sanfelice, Ricardo G.

Publikováno v: In IFAC PapersOnLine 2018 51(20):128-133

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An LMI approach to robust model predictive control of nonlinear systems with state-dependent uncertainties

Autor: Ojaghi, Pegah, Bigdeli, Nooshin, Rahmani, Mehdi

Publikováno v: In Journal of Process Control November 2016 47:1-10

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Active sensing in a bioinspired hand as an enabler of implicit curriculum learning for manipulation

Autor: Mir, Romina, Ojaghi, Pegah, Marjaninejad, Ali, Wehner, Michael, Valero-Cuevas, Francisco

The 9.5th international symposium on Adaptive Motion of Animals and Machines. Ottawa，Canada (Virtual Platform). 2021-06-22/25. Adaptive Motion of Animals and Machines Organizing Committee.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::161b847b00c3a6aaaf3080078fc53415
https://hdl.handle.net/11094/84900

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A Model Predictive Control Framework for Hybrid Dynamical Systems ⁎ ⁎ This research has been partially supported by the National Science Foundation under CAREER Grant no. ECS-1450484, Grant no. ECS-1710621, and Grant no. CNS-1544396, by the Air Force Office of Scientific Research under Grant no. FA9550-16-1-0015, by the Air Force Research Laboratory under Grant no. FA9453-16-1-0053, and by CITRIS and the Banatao Institute at the University of California

Autor: Altın, Berk, Ojaghi, Pegah, Sanfelice, Ricardo G

Publikováno v: IFAC-PapersOnLine, vol 51, iss 20

This paper presents a model predictive control (MPC) algorithm that asymptotically stabilizes a compact set of interest for a given hybrid dynamical system. The considered class of systems are described by a general model, which identifies the dynami

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::52b85be288a02760da6231642834a1fa
https://escholarship.org/uc/item/3s16d4vz

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