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Roberto Furfaro
Researcher at University of Arizona
Publications - 236
Citations - 2723
Roberto Furfaro is an academic researcher from University of Arizona. The author has contributed to research in topics: Reinforcement learning & Spacecraft. The author has an hindex of 22, co-authored 217 publications receiving 1806 citations. Previous affiliations of Roberto Furfaro include Polytechnic University of Turin.
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Deep reinforcement learning for six degree-of-freedom planetary landing
TL;DR: This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing.
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Asteroid Precision Landing via Multiple Sliding Surfaces Guidance Techniques
TL;DR: Based on high-order sliding-mode control theory, the proposed multiple sliding surface guidance algorithm has been designed to take advantage of the ability of the system to reach the sliding surface in a finite time as mentioned in this paper.
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GRS evidence and the possibility of paleooceans on Mars
James M. Dohm,Victor R. Baker,William V. Boynton,Alberto G. Fairén,J. C. Ferris,Michael Finch,Roberto Furfaro,Trent M. Hare,Daniel M. Janes,Jeffrey S. Kargel,Suniti Karunatillake,John Keller,Kris Kerry,Kyeong Ja Kim,Goro Komatsu,William C. Mahaney,Dirk Schulze-Makuch,Lucia Marinangeli,Gian Gabriele Ori,Javier Ruiz,Shawn J. Wheelock +20 more
TL;DR: The Gamma Ray Spectrometer (Mars Odyssey spacecraft) has revealed elemental distributions of potassium (K), thorium(Th), and iron (Fe) on Mars that require fractionation of K (and possibly Th and======Fe) consistent with aqueous activity as discussed by the authors.
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Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
TL;DR: The results show that, for most of the problems considered, X-TFC achieves high accuracy with low computational time, even for large scale PDEs, without suffering the curse of dimensionality.
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Reinforcement learning for angle-only intercept guidance of maneuvering targets
TL;DR: In this paper, the authors present a guidance law that uses observations consisting solely of seeker line-of-sight angle measurements and their rate of change, and demonstrate that the policy performs better than augmented zero-effort miss guidance with perfect target acceleration knowledge.