CAREER: Context-Aware Task-Oriented Dexterous Robotic Manipulation

职业：上下文感知、任务导向的灵巧机器人操作

基本信息

批准号：
2239540
负责人：
Hongsheng He
金额：
$ 59.96万
依托单位：
Wichita State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2028-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239540&HistoricalAwards=false
关键词：
CAREER Context Aware Task Oriented

项目摘要

With the advancement of modern industries, effective manipulation of unfamiliar objects in an assortment of dimensions, shapes, or materials becomes the bottleneck problem in the automation of manufacturing, services, and retail trade. This Faculty Early Career Development (CAREER) project will build innovative technologies to enable a robot to manipulate unfamiliar objects for difficult tasks, e.g., goods packing, drilling, waste sorting, and small part assembly. The advantage of the technologies is the capability of manipulating an object in accordance with the task requirements and the perceived object properties including shapes and materials. This project has great potential to benefit numerous industries by improving productivity, deskilling robot programming, simplifying robot planning, and lowering the technical boundaries. This project will support the national strategy in bringing manufacturing back to the US while benefiting many industries and increasing their economic competitiveness. The project will establish a holistic framework of Context-Aware Task-Oriented Manipulation (CATOM) to address three cornerstone challenges in dexterous manipulation: object affordance perception, dexterity modeling, and manipulation planning and learning. An object affordance refers to actions that match with the physical properties of an object, such as shapes, mass, and friction. The object affordances are obtained from object characteristics measured by multimodal sensors. For this purpose, a knowledge-driven model will be designed, which fuses heterogenous sensing and incorporates human experience. The object affordances will be represented by potential contacts under each grasp taxonomy. A topology-based modeling method will be used to align potential hand postures with the contacts for a proper grasp. With the topology-based modeling, a complex task will be represented as a spatial-temporal sequence of hand topologies and operations. A hybrid learning and planning mechanism will be implemented to deploy hand topologies and perform actions under contextual constraints. The research of these perception, planning, and learning methodologies will advance the knowledge in dexterous robotic manipulation for complex tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着现代行业的发展，在各种尺寸，形状或材料中有效操纵不熟悉的物体成为制造，服务和零售贸易自动化的瓶颈问题。这个教师的早期职业发展（职业）项目将建立创新的技术，以使机器人能够操纵陌生的物体处理艰巨的任务，例如，货物包装，钻孔，废物分类和小部分组装。该技术的优点是能够根据任务要求以及包括形状和材料在内的感知对象属性来操纵对象。该项目具有通过提高生产力，机器人编程，简化机器人计划并降低技术界限的巨大潜力，可以使众多行业受益。该项目将支持国家战略将制造业带回美国，同时使许多行业受益并提高其经济竞争力。该项目将建立一个以情境感知任务为导向的操纵（CATOM）的整体框架，以解决灵巧操作中的三个基石挑战：对象负担能力感知，敏捷性建模以及操纵计划和学习。对象负担得起的是与对象的物理特性相匹配的动作，例如形状，质量和摩擦。对象提供的是从通过多模式传感器测量的对象特性获得的。为此，将设计一个知识驱动的模型，该模型融合了异质的传感并结合了人类的经验。对象负担将由每个GRASP分类学下的潜在联系来代表。基于拓扑的建模方法将用于使潜在的手部姿势与触点保持适当的掌握。通过基于拓扑的建模，复杂的任务将被表示为手动拓扑和操作的时空序列。将实施混合学习和计划机制，以部署手部拓扑并在上下文约束下执行操作。对这些感知，计划和学习方法的研究将推进灵巧的机器人操纵的知识，以实现复杂任务。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来通过评估来支持的。