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

这个I-Corps项目更广泛的影响/商业潜力是为许多行业和公司开发一个潜在的平台，如先进制造业，电子商务，零售商，邮寄和物流。该平台可以彻底改变和自动化分拣和包装不规则形状物体的过程，为美国邮政分拣各种尺寸和重量的塑料邮袋;为沃尔玛和亚马逊包装不规则形状的货物。该项目还可以彻底改变依赖于先进制造中的通用工具的便携式自动化，例如，该项目的基础是开发一个平台系统，该平台系统将机器人抓取扩展到多指手的灵巧策略，并通过融合新型传感器提高上下文感知能力。为了实现灵巧的抓取和操作，设计了一个灵巧的机器人手，紧密结合拟人夹持力学，上下文感知和面向任务的抓取规划。机器人手通过融合多模态传感器来实现对对象启示的原位感知，包括对象尺寸和对象特性，例如材料，刚度和质量。这是第一个使用红外传感器和机器学习方法感知物体特征的机器人抓取器。为了针对不同的任务需求和对象启示部署有效的策略，开发了一个知识驱动的模型来解决灵巧操作运动的规划问题。该模型将人类知识表示为手部拓扑结构，并学习面向任务的操作计划以适应不同的工作环境。由于知识和学习的综合结构，该模型产生了强大的和适应性强的计划，学习效率高。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.