RI:Small: Leveraging Human Manipulation Skills to Advance Near Contact Robotic Grasping and In-Hand Stabilization

RI:Small：利用人类操纵技能推进近距离接触机器人抓取和手持稳定性

• 批准号: 1911050
• 负责人: Cindy Grimm
• 金额: $ 50万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911050&HistoricalAwards=false
• 关键词: RI; Small; Leveraging; Human; Manipulation; RI; Small; Leveraging; Human; Manipulation

People are incredibly good at picking up and manipulating objects. Robots, however, still struggle with reliably picking up objects. The goal of this project is to improve robotic grasping by observing how humans respond to challenging grasping situations. The project's central idea is that humans strive for grasp success rather than trying to find the best possible grasp. Humans use a grasp that is likely to work, even if they have misjudged where the object is, what shape it is, how heavy it is, etc. Finding what these strategies are (in a form a robot can use) will improve robotic grasping in the "real world". The technical challenge addressed in this project is how to go from human demonstrations all the way to robot hand control strategies. First, human user study participants will demonstrate grasping with actual robotic hands by "puppeteering" the hands through a structured set of grasping and manipulation tasks. This demonstration will then be reconstructed in a physics simulator. This provides a rich set of data in an environment - grounded in physical reality - that algorithms can learn in. Finally, the data is put in a novel form that is ideally suited to capturing what happens as the hand comes into contact with the object in a form suitable for machine learning. The result is a learned set of near-contact robotic controllers that can be incorporated into existing grasp planning algorithms in order to improve robotic grasping.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.

人们非常擅长拾起和操纵物体。但是，机器人仍然在可靠地拾起物体方面挣扎。该项目的目的是通过观察人类如何应对挑战性抓地情况来改善机器人抓地力。该项目的核心思想是，人类努力掌握成功，而不是试图找到最好的掌握。 人类使用可能起作用的掌握，即使他们错误地判断了物体所在的位置，它的形状，它的沉重程度等。找到这些策略是什么（以机器人可以使用的形式）将改善“现实世界”中的机器人抓地力。该项目中提出的技术挑战是如何从人类的示范到机器人手部控制策略。首先，人类用户研究参与者将通过一组结构化的握把和操纵任务“伪造”手来证明用实际的机器人手抓住。然后，该演示将在物理模拟器中重建。这在基于物理现实的环境中提供了一组丰富的数据，算法可以学习。最后，数据以一种新颖的形式放置，非常适合捕获随着手与对象接触的情况，以适合机器学习的形式接触。结果是一组知识渊博的近接触机器人控制器，可以将其纳入现有的GRASP计划算法中，以改善机器人掌握。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。