I-Corps: A Smart Context-Aware Multi-Fingered System for Dexterous Grasping

I-Corps：智能上下文感知多指系统，用于灵巧抓取

• 批准号: 2402466
• 负责人: Hongsheng He
• 金额: $ 5万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402466&HistoricalAwards=false
• 关键词: Corps; Smart; Context; Aware; Multi

The broader impact/commercial potential of this I-Corps project is the development of a potential platform for many industries and companies, such as advanced manufacturing, e-commerce, retailer, mailing, and logistics. The platform could revolutionize and automate the process of sorting and packing irregular-shaped objects, e.g., sorting plastic mail bags with various dimensions and weights for USPS; and packing irregular-shaped goods for Walmart and Amazon. The project could also revolutionize portable automation that relies on general tools in advanced manufacturing, e.g., enabling the large footprint of aircraft assembly in confined space access for Spirit AeroSystems; and automating manufacturing processes that require various kinds of manipulation such as placing, grasping, and capping.This I-Corps project is based on the development of a platform system extending robotic grasping towards dexterous strategies for multi-fingered hands, and increasing the capability of context perception by fusing novel sensors. To accomplish dexterous grasping and manipulation, a dexterous robotic hand was designed by tightly integrating anthropomorphic gripper mechanics, context perception, and task-oriented grasp planning. The robotic hand achieves in-situ perception of object affordances by fusing multimodal sensors, including object dimensions and object characteristics such as materials, rigidity, and mass. This is the first robotic gripper that perceives object characteristics by using infrared sensors and machine-learning methods. To deploy efficient strategies for various task requirements and object affordances, a knowledge-driven model is developed to resolve the planning of dexterous manipulation motion. The model represents human knowledge as hand topology and learns task-oriented manipulation plans to adapt to different work context. Due to the integrated structure of knowledge and learning, the model produces robust and adaptive plans with a high efficiency in learning.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.

该I-Corps项目的更广泛的影响/商业潜力是开发许多行业和公司的潜在平台，例如高级制造，电子商务，零售商，邮件和物流。该平台可以彻底改变并自动化分类和包装不规则形状的物体的过程，例如，对USP的各个尺寸和权重分类塑料邮件袋；并为沃尔玛和亚马逊包装不规则的商品。该项目还可以彻底改变依赖高级制造业中通用工具的便携式自动化，例如，在Spirit Aerosystems的限制空间访问中，可以实现大量的飞机组装；并自动化需要各种操纵的制造过程，例如放置，抓握和封盖。此I-Corps项目是基于平台系统的开发，该平台系统扩展了机器人抓地力，朝着灵巧的策略扩展了多指手手的手，并通过通过新颖的传感器来增强上下文感知的能力。为了完成灵活的抓握和操纵，灵巧的机器人手是通过紧密整合拟人化抓手力学，上下文感知和面向任务的抓握计划来设计的。机器人手通过融合多模式传感器（包括对象维度和物体特征，例如材料，刚度和质量）来实现对物体提供的原位感知。这是第一个通过使用红外传感器和机器学习方法来感知对象特性的机器人抓手。为了为各种任务要求和对象提供有效的策略，开发了一个知识驱动的模型来解决灵巧的操纵运动的计划。该模型代表人类知识作为手部拓扑，并学习了以任务为导向的操纵计划，以适应不同的工作环境。由于知识和学习的综合结构，该模型在学习方面产生了强大而适应性的计划。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来获得支持的。