隐式表示先验与黎曼几何结合用于贝叶斯机器人抓取
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
AI总结：
本文提出利用隐式表示构建场景相关先验，并结合基于仿真的贝叶斯推断算法，在黎曼流形上高效确定非结构化环境中的机器人抓取姿态，仿真与物理实验验证了其高成功率。
摘要：
在高噪声环境中进行机器人抓取面临复杂挑战，尤其是在对场景先验知识有限的情况下。具体而言，通过贝叶斯推断识别良好的抓取姿态变得困难，原因有二：i) 从无信息先验生成数据效率低下；ii) 后验通常涉及定义在黎曼流形上的复杂分布。在本研究中，我们探索使用隐式表示来构建依赖于场景的先验，从而能够应用高效的基于仿真的贝叶斯推断算法，在非结构化环境中确定成功的抓取姿态。仿真和物理基准测试的结果均展示了该方法的高成功率和巨大潜力。
英文摘要：
Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses with Bayesian inference becomes difficult due to two reasons: i) generating data from uninformative priors proves to be inefficient, and ii) the posterior often entails a complex distribution defined on a Riemannian manifold. In this study, we explore the use of implicit representations to construct scene-dependent priors, thereby enabling the application of efficient simulation-based Bayesian inference algorithms for determining successful grasp poses in unstructured environments. Results from both simulation and physical benchmarks showcase the high success rate and promising potential of this approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
