NRI: FND: Robust Grasping by Integrating Machine Learning with Physical Models

这个项目的目的是让机器人抓取东西变得可靠，这样机器人就可以捡起各种各样的物体，并将它们掉落的风险降到最低。除了工厂等精心控制的环境外，即使是目前最好的机器人操作系统，它们试图抓取和抬起的物体也会有超过5%的物体掉落。可靠的抓取将在社会的许多领域开启广泛的实际应用。例如，为老年人和残疾人提供的家庭援助需要能够抓取和移动物体，但掉下许多物体的机器人对许多用户来说并不有用。除了个人协助功能外，需要可靠抓取的商业应用范围从仓库和灵活制造到食品服务和清洁。接触感知对于机器人在非结构化环境中的可靠抓取是必不可少的，但现有的使用传感器信号的方法并不有效。之前使用接触传感创建可靠抓取的两种方法是基于物理的抓取分析和黑盒机器学习算法，但这两种方法在准确性和可靠性方面都被证明是不够的。目前尚不清楚这些限制是由于物理模型、学习算法还是传感器的缺陷。该项目将在精心控制的抓取实验中首次提供接触传感器的特性，而独立的传感器将为比较提供黄金标准。该项目将使用这些实验的数据来研究物理建模和机器学习方法的融合。通过更好地理解它们在预测抓握稳定性方面的特性，它将允许两种方法以一种保持各自最佳特性的方式结合在一起：来自物理模型的可解释性和概括性，以及来自机器学习的灵活性和准确性。通过将预测精度和可靠性推向顶峰，该项目还将确定传感器性能的限制，并首次根据任务要求定义所需的传感器信息。研究结果将作为非结构化环境下抓取和操纵系统的基础，并阐明传感器硬件、基于模型的信号解释和控制以及数据驱动的信号解释和控制之间的权衡。基于模型和数据驱动的方法的成功整合可能会成为许多其他领域的典范，在这些领域，模型是有用的，但不完整。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to make robot grasping reliable, so that robots can pick up a wide variety of objects with minimal risk of dropping them. Except for carefully-controlled settings like factories, even the best current robotic manipulation systems drop over 5% of the objects they attempt to grasp and lift. Reliable grasping would open a vast range of practical applications in many areas of society. For example, household assistance for the elderly and disabled requires the ability to grasp and move objects, but robots that drop many objects would not be useful for many users. In addition to personal assistance functions, commercial applications that require reliable grasping range from warehouses and flexible manufacturing to food service and cleaning. Contact sensing is essential for reliable robotic grasping in unstructured environments, but existing methods for using sensor signals have not been effective. The two prior approaches to creating reliable grasping using contact sensing were physics-based grasp analysis and black-box machine learning algorithms, and neither has proved sufficient in terms of accuracy and reliability. It is not clear whether the limitations were due to deficiencies in the physical models, learning algorithms, or sensors. This project will provide the first characterization of contact sensors in carefully-controlled grasping experiments, with independent sensors providing a gold standard for comparison. The project will use the data from those experiments to investigate merging the physical modeling and machine learning approaches. By providing an improved understanding of their properties in predicting grasp stability it will allow the joining of both approaches in a way that maintains the best properties of each: interpretability and generalizability from physical modeling and flexibility and accuracy from machine learning. By pushing prediction accuracy and reliability to its peak, the project will also characterize the limits on sensor performance and define, for the first time, the needed sensor information in terms of task requirements. The results will serve as the basis for grasping and manipulation systems for unstructured environments and spell out the tradeoffs among sensor hardware, model-based signal interpretation and control, and data-driven signal interpretation and control. The successful integration of model-based and data-driven approaches may serve as an exemplar for many other fields where models are useful but incomplete.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.