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contributor authorNganga, John N.
contributor authorWensing, Patrick M.
date accessioned2023-08-16T18:19:48Z
date available2023-08-16T18:19:48Z
date copyright2/28/2023 12:00:00 AM
date issued2023
identifier issn2689-6117
identifier otheraldsc_3_1_011002.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4291833
description abstractThis letter presents approaches that reduce the computational demand of including second-order dynamics sensitivity information into optimization algorithms for robots in contact with the environment. A full second-order differential dynamic programming (DDP) algorithm is presented where all the necessary dynamics partial derivatives are computed with the same complexity as DDP’s first-order counterpart, the iterative linear quadratic regulator (iLQR). Compared to linearized models used in iLQR, DDP more accurately represents the dynamics locally, but it is not often used since the second-order partials of the dynamics are tensorial and expensive to compute. This work illustrates how to avoid the need for computing the derivative tensor by instead leveraging reverse-mode accumulation of derivatives, extending previous work for unconstrained systems. We exploit the structure of the contact-constrained dynamics in this process. The performance of the proposed approaches is benchmarked with a simulated model of the MIT Mini Cheetah executing a bounding gait.
publisherThe American Society of Mechanical Engineers (ASME)
titleAccelerating Hybrid Systems Differential Dynamic Programming
typeJournal Paper
journal volume3
journal issue1
journal titleASME Letters in Dynamic Systems and Control
identifier doi10.1115/1.4056747
journal fristpage11002-1
journal lastpage11002-8
page8
treeASME Letters in Dynamic Systems and Control:;2023:;volume( 003 ):;issue: 001
contenttypeFulltext


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