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

机器人目前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。 该项目的目标是通过开发技术来缓解这一缺点，该技术将为机器人提供在间歇性接触任务中的准确实时感知。 随着机器人变得更有能力和自主性，项目成果将对操作任务产生重大影响。 PI还期望在其他领域获得成功应用，例如驱动增强现实系统中的实时触觉显示，从观察到的动觉演示中提取人类操作策略，以及识别模型参数以提高模拟准确性，更不用说提高水平了太空和海底探索的自主性。 在系统经历突然的状态转换并且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代表性不足的少数民族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，其中连续状态（即，姿态、速度和接触脉冲），以及离散的接触状态（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着扩展这项工作。 首先，他们将设计新的并行、随时互补问题（CP）求解器，以实现实时性能。 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对象、机器人链接和环境的更复杂几何模型。 该项目的智力价值在于三个主要活动：第一，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创造性但严格的技术过程，可以利用点云数据;第二，通过利用非光滑多体动力学和CPU/GPU计算系统的数学结构和属性实现实时性能;第三，以一种能够揭示估计准确性和速度之间的权衡的方式进行前两项活动。

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.