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.

翻译：在高噪声环境中实现机器人抓取存在复杂的挑战，特别是对于场景缺乏先验知识而言。尤其是由于以下两个原因，使用贝叶斯推断识别好的抓取姿势变得困难：i) 从无信息的先验中生成数据实际上效率低下，而且ii) 后验分布通常是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场景相关的先验，从而使得能够在非结构化环境下通过高效的基于模拟的贝叶斯推断算法识别出成功的抓取姿势。仿真和物理测试的结果展示了这种方法的高成功率和有前途的潜力。