Authors:
Christoforos Vlachos
and
Konstantinos Moustakas
Affiliation:
Department of Electrical and Computer Engineering, University of Patras, 26504 Rion-Patras, Greece
Keyword(s):
Configuration Space Signed Distance Function (CSDF), Sinusoidal Representation Networks (SIREN), Neural Implicit Representation, Robot Configuration Space, Signed Distance Function (SDF), Inverse Kinematics.
Abstract:
Signed Distance Functions (SDFs) are used in many fields of research. In robotics, many common tasks, such as motion planning and collision avoidance use distance queries extensively and, as a result, SDFs have been integrated widely in such tasks, fulfilling even the tightest speed requirements. At the same time, the idea of the more natural representation of distances directly in the configuration space (C-space) has been gaining ground, resulting in many interesting publications in the last few years. In this work, we aim to define a C-space Signed Distance Function (CSDF) in a way that parallels other SDF definitions. Additionally, coupled with recent advancements in machine learning and neural representation of implicit functions, we attempt to create a neural approximation of the CSDF in a way that is fast and accurate. To validate our contributions, we construct an experiment environment to test the accuracy of our proposed workflow in an inverse kinematics contact test. Compa
ring these results to the performance of another published approach to the neural implicit representation of distances in the Configuration Space, we found that our method offers a considerable improvement, reducing the measured errors and increasing the success rate.
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