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.

翻译：通用机器人夹爪因其丰富的非光滑接触动力学特性以及环境或传感器噪声带来的多种不确定性源，控制起来具有挑战性。在本工作中，我们展示了如何通过对机器人及其环境进行完整的随机正向仿真，利用基于仿真的贝叶斯推理计算六自由度抓取位姿，同时稳健地处理系统中的多种不确定性。采用保持旋转空间非线性的黎曼流形优化过程，计算最大后验抓取位姿。仿真与物理基准测试表明，该方法具有可观的高成功率。