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Adina M. Panchea
Researcher at University of Orléans
Publications - 15
Citations - 118
Adina M. Panchea is an academic researcher from University of Orléans. The author has contributed to research in topics: Computer science & Optimal control. The author has an hindex of 4, co-authored 12 publications receiving 76 citations. Previous affiliations of Adina M. Panchea include Université Paris-Saclay & Université de Sherbrooke.
Papers
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Proceedings ArticleDOI
Human motion segmentation using cost weights recovered from inverse optimal control
Jonathan Feng-Shun Lin,Vincent Bonnet,Adina M. Panchea,Nacim Ramdani,Gentiane Venture,Dana Kulic +5 more
TL;DR: A method to segment human movement by detecting changes to the optimization criterion being used via inverse optimal control is proposed, achieving a segmentation accuracy of 84%.
Proceedings ArticleDOI
Towards solving inverse optimal control in a bounded-error framework
Adina M. Panchea,Nacim Ramdani +1 more
TL;DR: This paper proposes a bounded-error approach to inverse optimal control; where all uncertainty and disturbances acting on observation or modeling are assumed bounded but otherwise unknown, and compute bounds on the set of criteria that make the uncertain observations optimal.
Proceedings ArticleDOI
Human Arm Motion Analysis Based on the Inverse Optimization Approach
TL;DR: Clues are given on which basis of objective functions can be relevant for analyzing a human arm motion and/or can be extended to the use of more complex models with more degrees-of-freedom.
Proceedings ArticleDOI
Extended reliable robust motion planners
TL;DR: A new method to plan guaranteed to be safe paths in an uncertain environment, with an uncertain initial and final configuration space, while avoiding static obstacles is presented, based on optimal Rapidly-exploring Random Trees algorithm and using interval analysis.
Dissertation
Inverse optimal control for redundant systems of biological motion
TL;DR: This thesis addresses inverse optimal control problems (IOCP) to find the cost functions for which the human motions are optimal, and proposed a new approach to solving the IOCP, in a bounded error framework.