In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of interest. Motivated by this, in this work we model high-precision insertion tasks as planning problems under pose uncertainty, where we effectively utilize the occurrence of contacts (or the lack thereof) as observations to reduce uncertainty and reliably complete the task. We present a preprocessing-based planning framework for high-precision insertion in repetitive and time-critical settings, where the set of initial pose distributions (identified by a perception system) is finite. The finite set allows us to enumerate the possible planning problems that can be encountered online and preprocess a database of policies. Due to the computational complexity of constructing this database, we propose a general experience-based POMDP solver, E-RTDP-Bel, that uses the solutions of similar planning problems as experience to speed up planning queries and use it to efficiently construct the database. We show that the developed algorithm speeds up database creation by over a factor of 100, making the process computationally tractable. We demonstrate the effectiveness of the proposed framework in a real-world plug insertion task in the presence of port position uncertainty and a pipe assembly task in simulation in the presence of pipe pose uncertainty.

翻译：在诸如插头插入或装配等对位姿估计误差容忍度较低（误差量级为2毫米即可导致任务失败）的操控任务中，利用触觉/接触模态有助于精确定位目标物体。受此启发，本研究将高精度插入任务建模为位姿不确定性下的规划问题，其中我们有效利用接触发生（或未发生）作为观测信息以减少不确定性并可靠完成任务。我们提出一种基于预处理的规划框架，适用于重复性且时间紧迫的高精度插入场景，其中初始位姿分布集合（由感知系统识别）是有限的。该有限集合允许我们枚举在线可能遇到的所有规划问题，并预处理一个策略数据库。鉴于构建该数据库的计算复杂性，我们提出一种通用的基于经验的POMDP求解器E-RTDP-Bel，该求解器利用相似规划问题的解作为经验来加速规划查询，并借此高效构建数据库。实验表明，所开发算法将数据库创建速度提升超过100倍，使得该过程在计算上易于处理。我们通过存在端口位置不确定性的真实世界插头插入任务，以及存在管道位姿不确定性的仿真管道装配任务，验证了所提框架的有效性。