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Andreas Müller

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3 papers

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JMLR Journal 2021 Journal Article

OpenML-Python: an extensible Python API for OpenML

Matthias Feurer
Jan N. van Rijn
Arlind Kadra
Pieter Gijsbers
Neeratyoy Mallik
Sahithya Ravi
Andreas Müller
Joaquin Vanschoren

OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper, we introduce OpenML-Python, a client API for Python, which opens up the OpenML platform for a wide range of Python-based machine learning tools. It provides easy access to all datasets, tasks and experiments on OpenML from within Python. It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML. Furthermore, it comes with a scikit-learn extension and an extension mechanism to easily integrate other machine learning libraries written in Python into the OpenML ecosystem. Source code and documentation are available at https://github.com/openml/openml-python/. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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ICRA Conference 2017 Conference Paper

Recursive second-order inverse dynamics for serial manipulators

Andreas Müller

Various model-based control schemes, e. g. feedback-linearizing control of robotic manipulators with series elastic actuators require time derivatives of the control forces, i. e. of the equations of motion. This is referred to as the higher-order inverse dynamics problem. As this must be evaluated on the controller hardware, computationally efficient formulations are crucial. While recursive O (n) inverse dynamics algorithms are well-established, such for the higherorder inverse dynamics are relatively new. O (n) algorithms for the latter problem were published recently building upon the known recursive inverse dynamics algorithms in terms of Denavit-Hartenber (DH) parameters. The DH convention is restrictive, however. Also the higher-order formulations are naturally very complex. Both issues are addresses in this paper with concepts from screw and Lie group theory. An O (n) algorithm for determining the first and second time derivative of the inverse dynamics solution is presented. The Lie group approach provides a high level of compactness while ensuring the computational efficiency. Being frame invariant this formulation allows for using representations in different frame. Two formulations are presented, one using the body-fixed and the other the so-called hybrid representation of twists. The latter is deemed to be computationally more efficient that the classical body-fixed version. Results are shown for a 6-DOF industrial manipulator.

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ICRA Conference 2014 Conference Paper

Closed form expressions for the sensitivity of kinematic dexterity measures to posture changing and geometric variations

Andreas Müller

This paper addresses the sensitivity of dexterity measures w. r. t. the posture of a manipulator, with given design parameters, as well as w. r. t. the manipulator's geometric parameters. These are required for placing a manipulator so to maximize dexterity and for the optimal layout of the link geometry, respectively. Explicit expressions are derived for first and second partial derivatives of dexterity measures w. r. t. to joint angles and w. r. t. geometric link parameters. The latter is obtained using a virtual joint method extending the product of exponentials formula for the forward kinematics. The approach applies to serial and parallel manipulators.

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