For many robotic manipulation and contact tasks, it is crucial to accurately estimate uncertain object poses, for which certain geometry and sensor information are fused in some optimal fashion. Previous results for this problem primarily adopt sampling-based or end-to-end learning methods, which yet often suffer from the issues of efficiency and generalizability. In this paper, we propose a novel differentiable framework for this uncertain pose estimation during contact, so that it can be solved in an efficient and accurate manner with gradient-based solver. To achieve this, we introduce a new geometric definition that is highly adaptable and capable of providing differentiable contact features. Then we approach the problem from a bi-level perspective and utilize the gradient of these contact features along with differentiable optimization to efficiently solve for the uncertain pose. Several scenarios are implemented to demonstrate how the proposed framework can improve existing methods.

翻译：对于许多机器人操作和接触任务而言，准确估计不确定物体位姿至关重要，这需要以最优方式融合特定几何与传感器信息。已有方法主要采用基于采样或端到端学习的方式，但往往存在效率与泛化能力不足的问题。本文提出一种新颖的可微框架用于接触过程中的不确定位姿估计，从而能够通过基于梯度的求解器高效准确地求解。为此，我们引入一种具有高度适应性且能提供可微接触特征的新型几何定义。随后从双层优化视角出发，利用这些接触特征的梯度与可微优化方法高效求解不确定位姿。通过多个场景实验验证了所提框架对现有方法的改进效果。