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Rigid body dynamics algorithms play a crucial role in several components of a robot controller and simulations. Real time constraints in high frequency control loops and time requirements of specific applications demand these functions to be very efficient. Despite the availability of established algorithms, their efficient implementation for a specific robot still is a tedious and error-prone task. However, these components are simply necessary to get high performance controllers. To achieve efficient yet well maintainable implementations of dynamics algorithms we propose to use a domain specific language to describe the kinematics/dynamics model of a robot. Since the algorithms are parameterized on this model, executable code tailored for a specific robot can be generated, thanks to the facilities available for dsls. This approach allows the users to deal only with the high level description of their robot and relieves them from problematic hand-crafted development; resources and efforts can then be focused on open research questions. Preliminary results about the generation of efficient code for inverse dynamics will be presented as a proof of concept of this approach.
Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both
This paper has been withdrawn.
If robots are ever to achieve autonomous motion comparable to that exhibited by animals, they must acquire the ability to quickly recover motor behaviors when damage, malfunction, or environmental conditions compromise their ability to move effective
Mechanism calibration is an important and non-trivial task in robotics. Advances in sensor technology make affordable but increasingly accurate devices such as cameras and tactile sensors available, making it possible to perform automated self-contai
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have been used to learn models of robot dynamics from data, but they suffer f