Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

翻译：鲁棒抓取是机器人学中一个主要且尚未解决的问题。物体的三维形状信息可通过先验知识（例如已知物体的精确模型或常见物体的近似模型）或实时感知（例如未知物体的部分点云）获取，并用于识别潜在的高质量抓取位姿。然而，由于建模与感知误差，通常需要通过局部探索来优化这些抓取位姿，并使其成功应用于现实场景。近期提出的无迹贝叶斯优化技术能够通过选择对输入空间不确定性（如抓取执行误差）具有鲁棒性的抓取方案，使此类探索更加安全。在先前关于二维优化研究的基础上，本文提出一种三维触觉探索策略，将无迹贝叶斯优化与新型碰撞惩罚启发式算法相结合，以高效方式寻找安全抓取：通过将搜索空间扩展至三维，我们能够找到更优的抓取位姿，而碰撞惩罚启发式算法则使我们能够在无需增加探索步骤的情况下实现这一目标。