NRI: Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and other Contact Tasks (Continuation)

目前机器人的一个弱点是它们无法在非结构化环境中快速可靠地执行接触任务。这个项目代表了两个合作机构的教员之间的合作，其目标是通过开发技术来减轻这一缺点，使机器人能够在间歇性接触的任务中获得准确的实时感知。随着机器人变得更有能力和更自主，项目结果将对操纵任务产生强烈影响。pi还希望在其他领域取得成功，例如，在增强现实系统中驱动实时触觉显示，从观察到的动觉演示中提取人类操作策略，并确定模型参数以提高仿真精度，更不用说提高空间和海底探索的自主水平。机器人之外的其他应用预计在系统经历突然状态转换的情况下，目标是状态估计或实时反馈控制（例如，化学，金融和地质系统）。pi的实验室在支持女性和少数族裔方面有着良好的记录，这项研究将被整合到两个校区研究生和本科生的各种教学活动中。在之前的工作中，该团队提出了DBC-SLAM框架，其中跟踪被操纵对象的连续状态（即姿态、速度和接触脉冲）和离散接触状态（即接触-非接触和粘滑），并估计重要的模型参数。在这项研究中，他们将在两个方向上显著地扩展这项工作。首先，他们将设计新的并行、随时互补问题（CP）求解器，以获得实时性能。其次，他们将增强DBC-SLAM中的动态贝叶斯模型，以允许使用点云观测和更复杂的物体、机器人链接和环境的几何模型。该项目的智力价值在于三个主要活动：首先，创造性的，但严格的，基于非光滑力学和贝叶斯估计的基本第一原理设计感知算法的技术过程，可以利用点云数据；其次，利用非光滑多体动力学和CPU/GPU计算系统的数学结构和特性实现实时性能；第三，以一种阐明评估准确性和速度之间权衡的方式来执行前两个活动。

A current weakness of robots is their inability to quickly and reliably perform contact tasks in unstructured environments. The goal of this project, which represents a collaboration between faculty at two partner institutions, is to alleviate this shortcoming by developing techniques that will afford robots accurate real-time perception in tasks exhibiting intermittent contact. Project outcomes will have a strong impact in manipulation tasks, as robots become more capable and autonomous. The PIs also expect successful applications in other areas, for instance to drive real-time haptic displays in augmented reality systems, to extract human manipulation strategies from observed kinesthetic demonstrations, and to identify model parameters to improve simulation accuracy, not to mention in advancing the level of autonomy for space and undersea exploration. Additional applications outside of robotics are anticipated in situations where a system experiences abrupt state transitions and the goal is either state estimation or real-time feedback control (e.g., chemical, financial, and geological systems). The PIs' labs have a track record of supporting women and under-represented minorities, and the research will be integrated into a variety of pedagogical activities at the graduate and undergraduate level on both campuses.In previous work the team proposed the DBC-SLAM framework, in which continuous states (i.e., poses, velocities and contact impulses), and discrete contact states (i.e., contact-noncontact and stick-slip) of the manipulated objects, are tracked and important model parameters are estimated. In this research, they will extend that work significantly in two directions. First, they will design new parallel, anytime complementarity problem (CP) solvers in order to attain real-time performance. Second, they will enhance the dynamic Bayesian models in DBC-SLAM to allow the use of point-cloud observations and more complex geometric models of the objects, robot links, and environment. The intellectual merit of the project lies in three main activities: first, the creative, yet rigorous, technical process of designing perception algorithms based on fundamental first principles of nonsmooth mechanics and Bayesian estimation in a way that can utilize point-cloud data; second, achieving real-time performance by exploiting the mathematical structure and properties of both the nonsmooth multibody dynamics and CPU/GPU computing systems; and third, pursuing the first two activities in a way that sheds light on the trade-offs between estimation accuracy and speed.