Robot Haptics: Probabilistic Methods for Active Perception

机器人触觉：主动感知的概率方法

• 批准号: 0535289
• 负责人: J. Salisbury
• 金额: --
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535289&HistoricalAwards=false
• 关键词: Robot; Haptics; Probabilistic; Methods; Active

The primary purpose of the project is to develop methods that dramatically increase the utility and reliability of a robot's sense of touch. The PIs' proposed study of robot haptics (perception and manipulation through active touch interaction) particularly addresses the use of robots in unstructured situations where little may be known about the objects to be manipulated and where visual information alone is not sufficient to guarantee robust task performance.This research will consist of three main components:1. Probabilistic sensor processing methods for haptics. The strength of probabilistic techniques is their ability to fuse together many streams of noisy sensor data (such as contact information) based on a given physical model.2. New active perceptual methods. As with human senses, active exploration is required to provide sensors with a rich source of information about physical properties of the world.3. Autonomous strategy selection. Often it is necessary to switch between manipulation and exploration in order to accomplish a task. A key contribution of this research will be a decision-theoretic probabilistic method for determining when manipulation is likely to succeed and when further haptic exploration is required.In order to support the proposed robot haptics research, the construction of the Probabilistic Manipulation Experiment Table (PMET) is proposed. The PMET will consist of one or two small manipulating arms with instrumented 'fingertip' end effecters that support force sensors, accelerometers, and tactile sensors. An interactive control software package, the Probabilistic Robotics Studio (PRS), will be developed to provide a graphical interface to configure experiments and view probabilistic models in real-time. Initial phases of research will develop probabilistic models and exploration strategies that characterize properties of contact (including contact slip) and non-visual properties of objects (frictional coefficients, center of mass, etc.).Intermediate phases will demonstrate autonomous strategy selection during robust one-finger manipulation of objects in the plane. The final phases will include two-finger exploration, two finger manipulation, and lifting objects.Increased understanding of touch sensory information from this work will give the robot design community better construction techniques for robot hands and their sensor systems. The PMET and PRS will provide an interactive experimental platform that can be used to train robotics students and explore new ideas. The PIs plan summer internships for high school students in the area around Stanford University to allow experimentation with the PRS. In addition, the PIs will aid the interns in development of presentations for K-12 students.

该项目的主要目的是开发能够显著提高机器人触觉的实用性和可靠性的方法。PIS提出的机器人触觉(通过主动触摸交互来感知和操纵)的研究特别针对机器人在非结构化环境中的使用，其中对要操作的对象知之甚少，并且仅凭视觉信息不足以保证稳健的任务性能。这项研究将由三个主要部分组成：1.触觉的概率传感器处理方法。概率技术的优势在于它们能够基于给定的物理模型将许多噪声传感器数据流(如联系信息)融合在一起。新的主动感知方法。就像人类的感官一样，需要积极的探索，为传感器提供关于世界物理特性的丰富信息来源。自主战略选择。为了完成一项任务，经常需要在操作和探索之间切换。为了支持机器人触觉研究，提出了概率操纵实验表(PMET)的构建。PMET将由一个或两个带有仪表化“指尖”末端执行器的小型操作臂组成，这些末端执行器支持力传感器、加速计和触觉传感器。将开发一个交互控制软件包，概率机器人工作室(PRS)，以提供一个图形界面，以配置实验和实时查看概率模型。研究的初始阶段将开发概率模型和探索策略，以表征接触属性(包括接触滑移)和对象的非视觉属性(摩擦系数、质心等)。中间阶段将演示在稳健的一根手指操纵平面上的对象时自主选择策略。最后的阶段将包括两个手指探索、两个手指操作和举起物体。通过这项工作增加对触摸感觉信息的理解，将为机器人设计界提供更好的机器人手及其传感器系统的构建技术。PMET和PRS将提供一个互动的实验平台，可以用来培训机器人专业的学生并探索新的想法。PI计划在斯坦福大学附近地区为高中生提供暑期实习机会，以便进行PR的实验。此外，PIS将帮助实习生为K-12学生开发演示文稿。