RI: Medium: Robotic Assistance with Dressing using Simulation-Based Optimization

RI：中：使用基于模拟的优化进行穿衣机器人辅助

• 批准号: 1514258
• 负责人: Charles Kemp
• 金额: $ 120万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514258&HistoricalAwards=false
• 关键词: RI; Medium; Robotic; Assistance; Dressing

The aging population, rising healthcare costs, and shortage of healthcare workers in the United States create a pressing need for affordable and effective personalized care. Physical disabilities due to illness, injury, or aging can result in people having difficulty dressing themselves, and the healthcare community has found that dressing is an important task for independent living. The goal of this research is to develop techniques that enable robots to assist people with putting on clothing, which is a challenging task for robots due to the complexities of cloth, the human body, and robots. A key aspect of this research is that robots will discover how they can help people by quickly trying out many options in a computer simulation. Success in this research would make progress towards robots capable of giving millions of people greater independence and a higher quality of life. In addition to healthcare applications, this research will result in better computer tools for fruitful collaborations between robots and humans in other scenarios.This research uses efficient physics simulation and optimization tools to substantially automate the design of assistive robots for dressing. The approach considers the robot to be an assistive device that a human learns to use. The system optimizes the assistive robot based on what a particular human with impairments is capable of doing comfortably, rather than what he/she typically does. This approach automatically optimizes personalized assistive controllers for a particular user and article of clothing via simulation. Due to frequent line-of-sight occlusion and the importance of controlling forces applied to the user's body, controllers that use data-driven haptic perception are trained using simulation-generated data. These capabilities critically depend on advancements in the efficient physical simulation of cloth, robots, and humans, as well as the discovery of appropriate human motions for a given assistive robot. This work advances the state of the art in assistive robotics, haptic perception, human modeling, optimization and efficient physical simulation. Evaluation of the system is in simulation and in the real world with test rigs that model aspects of dressing, a PR2 robot dressing a humanoid robot, and a PR2 dressing able-bodied participants with restricted motion.

美国人口老龄化、医疗保健成本上升以及医护人员短缺，迫切需要负担得起且有效的个性化护理。由于疾病，受伤或衰老导致的身体残疾可能导致人们难以自己穿衣，医疗保健界发现穿衣是独立生活的重要任务。 这项研究的目标是开发技术，使机器人能够帮助人们穿上衣服，这是一项具有挑战性的任务，因为衣服，人体和机器人的复杂性。这项研究的一个关键方面是，机器人将通过在计算机模拟中快速尝试许多选项来发现它们如何帮助人们。 这项研究的成功将使机器人能够为数百万人提供更大的独立性和更高的生活质量。除了医疗保健应用之外，这项研究还将为机器人和人类在其他场景中的富有成效的合作提供更好的计算机工具。这项研究使用高效的物理模拟和优化工具，基本上实现了穿衣辅助机器人设计的自动化。该方法将机器人视为人类学习使用的辅助设备。该系统根据特定的有障碍的人能够舒适地做什么，而不是他/她通常做什么来优化辅助机器人。这种方法通过模拟自动优化针对特定用户和服装的个性化辅助控制器。由于频繁的视线遮挡和控制施加到用户身体的力的重要性，使用数据驱动的触觉感知的控制器使用模拟生成的数据进行训练。这些能力关键取决于在布料，机器人和人类的有效物理模拟的进步，以及为给定的辅助机器人发现适当的人类运动。这项工作推进了辅助机器人技术，触觉感知，人体建模，优化和有效的物理模拟的最新技术。该系统的评估是在模拟和在真实的世界与测试台，模型方面的着装，一个PR 2机器人打扮的人形机器人，和一个PR 2服装健全的参与者与限制运动。