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

美国人口老龄化、医疗成本上升和医护人员短缺，对负担得起的有效个性化护理产生了迫切的需求。由于疾病、伤害或衰老导致的肢体残疾可能会导致人们难以穿衣，医疗保健社区发现穿衣是独立生活的一项重要任务。这项研究的目标是开发使机器人能够帮助人们穿衣服的技术，由于衣服、人体和机器人的复杂性，这对机器人来说是一项具有挑战性的任务。这项研究的一个关键方面是，机器人将发现它们如何通过在计算机模拟中快速尝试许多选项来帮助人们。这项研究的成功将使机器人朝着能够给数百万人更大的独立性和更高质量的生活的方向迈进。除了医疗保健应用，这项研究还将带来更好的计算机工具，用于机器人和人类在其他场景中卓有成效的合作。这项研究使用高效的物理模拟和优化工具，基本上自动化了辅助穿衣机器人的设计。该方法将机器人视为人类学习使用的辅助设备。该系统基于特定的有残疾的人能够舒适地做什么，而不是他/她通常做的事情，来优化辅助机器人。该方法通过模拟自动优化针对特定用户和衣物的个性化辅助控制器。由于频繁的视线遮挡和控制施加在用户身体上的力的重要性，使用数据驱动触觉的控制器使用模拟生成的数据进行训练。这些能力关键依赖于对布料、机器人和人类的有效物理模拟方面的进步，以及为给定的辅助机器人发现适当的人类运动。这项工作推动了辅助机器人、触觉感知、人体建模、优化和高效物理模拟方面的最新进展。对该系统的评估是在模拟和真实世界中进行的，其中包括模拟着装方面的测试台、为类人机器人着装的PR2机器人以及为运动受限的健全参与者着装的PR2机器人。

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.