NRI-Small: Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks

机器人目前无法在非结构化环境中快速或可靠地抓取物体或执行其他接触任务。 该项目正在开发支持接触任务的准确实时感知技术。 在所提出的方法中，传感器数据跟踪被操纵对象的连续运动，同时更新对象的模型。 粒子滤波，一种蒙特-卡罗模拟，确保这种跟踪和更新的一致性。这项工作的最大影响将是在机器人抓取和操纵。 由于建模和概率推理的综合，可以预期进一步的影响，例如在用于远程呈现的实时触觉中。

Robots cannot currently grasp objects or perform other contact tasks in unstructured environments with speed or reliability. This project is developing techniques for accurate real-time perception in support of contact tasks. In the proposed method, sensor data tracks the continuous motions of manipulated objects, while models of the objects are simultaneously updated. Particle filtering, a kind of Monte-Carlo simulation, ensures consistency of this tracking and updating.The strongest impact of this work will be in robotic grasping and manipulation. Because of the synthesis of modeling and probabilistic inference, further impacts can be expected, for example in real-time haptics for telepresence.