Abstract
We have developed a formalism for describing and analyzing a very simple representative of a class of robotic tasks which involve repeated robot-environment interactions, among them the task of juggling. We review our empirical success to date with a new class of control algorithms for this task domain that we call “mirror algorithms.” These new nonlinear feedback algorithms were motivated strongly by experimental insights after the failure of local controllers based upon a linearized analysis. We offer here a proof that a suitable mirror algorithm is correct with respect to the local version of a specified task — the “vertical one-juggle” — but observe that the resulting ability to place poles of the local linearized system does not achieve noticeably superior transient performance in experiments. We discuss the further analysis and experimentation that should provide a theoretical basis for improving performance.
This work has been supported in part by PMI Motion Technologies, INMOS Corporation and the the National Science Foundation under a Presidential Young Investigator Award held by the second author.
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Bühler, M., Koditschek, D.E., Kindlmann, P.J. (1990). A simple juggling robot: Theory and experimentation. In: Hayward, V., Khatib, O. (eds) Experimental Robotics I. Lecture Notes in Control and Information Sciences, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0042512
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DOI: https://doi.org/10.1007/BFb0042512
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