Simulation-based Bayesian inference for robotic grasping
- URL: http://arxiv.org/abs/2303.05873v1
- Date: Fri, 10 Mar 2023 11:56:56 GMT
- Title: Simulation-based Bayesian inference for robotic grasping
- Authors: Norman Marlier, Olivier Br\"uls and Gilles Louppe
- Abstract summary: General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise.
In this work, we demonstrate how to compute 6-DoF grasp poses using simulation-based Bayesian inference.
- Score: 6.218934678555297
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: General robotic grippers are challenging to control because of their rich
nonsmooth contact dynamics and the many sources of uncertainties due to the
environment or sensor noise. In this work, we demonstrate how to compute 6-DoF
grasp poses using simulation-based Bayesian inference through the full
stochastic forward simulation of the robot in its environment while robustly
accounting for many of the uncertainties in the system. A Riemannian manifold
optimization procedure preserving the nonlinearity of the rotation space is
used to compute the maximum a posteriori grasp pose. Simulation and physical
benchmarks show the promising high success rate of the approach.
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