Automatic differentiation of rigid body dynamics for optimal control and estimation |
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Authors: | Markus Giftthaler Michael Neunert Markus Stäuble Marco Frigerio Claudio Semini Jonas Buchli |
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Affiliation: | 1. Agile &2. Dexterous Robotics Lab, Institute of Robotics and Intelligent Systems, ETH Zürich, Switzerland.mgiftthaler@ethz.ch;4. Dexterous Robotics Lab, Institute of Robotics and Intelligent Systems, ETH Zürich, Switzerland.;5. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy. |
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Abstract: | AbstractMany algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with rigid body dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool ‘RobCoGen’ to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a trajectory optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a six-DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning. |
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Keywords: | Automatic differentiation rigid body dynamics trajectory optimization numerical optimal control |
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