Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

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Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning
Title:
Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning
Other Titles:
2021 IEEE International Conference on Robotics and Automation (ICRA)
Keywords:
Publication Date:
18 October 2021
Citation:
Acar, C., & Tee, K. P. (2021). Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning. 2021 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra48506.2021.9561456
Abstract:
Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISBN:
978-1-7281-9077-8
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