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

该项目旨在使机器人抓取可靠，以便机器人能够以最小的风险拾取各种各样的物体。除了像工厂这样精心控制的环境外，即使是目前最好的机器人操作系统，它们试图抓住和举起的物体也会掉下5%以上。可靠的抓取将在社会的许多领域开辟广泛的实际应用。例如，为老年人和残疾人提供的家庭帮助需要能够抓住和移动物体，但是掉落许多物体的机器人对许多用户来说并不有用。除了个人辅助功能外，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。接触感测对于非结构化环境中的可靠机器人抓取是必不可少的，但是用于使用传感器信号的现有方法并不有效。使用接触感测创建可靠抓取的两种先前方法是基于物理的抓取分析和黑盒机器学习算法，并且在准确性和可靠性方面都没有被证明是足够的。目前尚不清楚这些限制是否是由于物理模型、学习算法或传感器的缺陷。该项目将提供接触传感器在仔细控制的抓取实验中的第一个表征，独立传感器提供了比较的金标准。该项目将使用这些实验的数据来研究合并物理建模和机器学习方法。通过提供对它们在预测抓握稳定性方面的属性的更好理解，它将允许以保持每种方法的最佳属性的方式加入这两种方法：物理建模的可解释性和可推广性以及机器学习的灵活性和准确性。通过将预测准确性和可靠性推向顶峰，该项目还将描述传感器性能的极限，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以作为模型有用但不完整的许多其他领域的典范。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。

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.