NRI: FND: Controllable Compliance: A New Robotic Arm for Contact-Rich Manipulation

残疾人、体弱多病者和老年人将受益于家庭中有能力的服务机器人，它们可以协助完成日常生活任务。拥有类似人类能力的手臂和手的机器人可以帮助捡回掉落或远处的物体，并帮助烹饪、清洁和梳理。这将提高人们的生活质量和独立性，并大大减轻照顾者的负担。虽然如今几乎每家工厂都有机器人，但它们生活在安全屏障后面，远离人类。传统机器人擅长控制自己的运动，但不擅长控制它们在接触物体时施加的力；从本质上讲，它们是精确的，但不是温和的。这项研究引入了一种设计机器人的新方法，当它们与人和物体接触时，它们可以很容易地感觉到。一种新的设计允许所有移动手臂的马达都位于机器人的基座上。传统的机器人在手臂的每个关节上都安装了一个电机，这使得机器人非常重。当机器人变得更轻，移动更平缓时，不仅会使它们更安全，而且也更容易编程，让它们学会如何拿起和操纵物体。传统上，机器人仅依靠摄像头来检测物体并计划如何抓取它们，但在这项研究中，机器人也将适应并学习通过触觉与物体互动。可靠和安全的操作是实现自主和半自主辅助机器人愿景的先决条件，特别是要实现增加人们独立生活的时间长度和质量的目标。关键的挑战是开发机械手硬件，它可以准确而轻柔地移动，并支持与人和精致物体的安全接触丰富的物理交互。在这项工作中，使用了远程直接驱动（RDD）配置：所有电机都安装在底座上，通过滚动膜片密封静压传动远程连接到手臂和手的关节，这使得手臂的运动质量非常小。大半径直接驱动电机用于实现最小的关节静摩擦，并允许在大范围内调节关节顺应性。因此，手臂表现出光滑的二阶关节动力学，使得使用优化方法规划富含接触的轨迹或使用离线深度强化学习学习富含接触的策略更容易。此外，可控的顺应性将使我们的操作策略能够在需要时使用顺应性来适应环境的不确定性，但在需要高精度时使用高刚度。与传统机械臂相比，这种类型的机械臂具有更轻、摩擦更小、更固有的可反向驱动性的潜力，为人机交互提供了更高的安全性，并增加了自主系统可以承担的物理人类辅助任务的范围。该奖项反映了美国国家科学基金会的法定使命，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。

People with disabilities, the infirm, and the elderly would benefit from capable service robots in the home that could assist with daily living tasks. Robots with arms and hands that have human-like abilities could help retrieve dropped or distant objects, and help with cooking, cleaning, and grooming. This would increase people's quality of life and independence, and greatly reduce the burden on their caregivers. While robots are found in almost every factory today, they live behind safety barriers, out of reach of people. Traditional robots are good at controlling their motion, but bad at controlling the forces they apply when touching objects; essentially, they are precise but not gentle. This research introduces a new way to design robots that allows them to easily feel when they are in contact with people and objects. A new design allows all of the motors that move the arms to be located in the robot's base. A traditional robot has a motor mounted at each joint in the arm, making the robot very heavy. When robots are lighter and move more gently, it does not just make them safer, but it also makes it easier to program them to learn how to pick up and manipulate objects. Traditionally, robots depend only on cameras to detect objects and plan how to grab them, but in this research, robots will also adapt and learn to interact with objects through their sense of touch.Reliable and safe manipulation is a prerequisite for the vision of autonomous and semi-autonomous assistive robots, particularly towards the goal of increasing the length and quality of time that people can live independently. The key challenge is developing manipulator hardware that can move accurately but gently and supports safe contact-rich physical interaction with people and delicate objects. In this work a remote-direct-drive (RDD) configuration is used: all motors are mounted in the base, remotely connected to joints in the arm and hand via rolling-diaphragm sealed hydrostatic transmissions, which keeps the arm moving mass very small. Large radius direct-drive motors are used to achieve minimal joint static friction and allow joint compliance to be modulated over a wide range. Thus, the arm exhibits smooth second order joint dynamics, making it easier to plan contact-rich trajectories using optimization methods or learn contact-rich policies using off-line deep reinforcement learning. Moreover, the controllable compliance will enable our manipulation policies to use compliance when needed to accommodate environmental uncertainty but high stiffness when high precision is required. This type of manipulator has the potential to be lighter, lower friction, and more inherently backdrivable than traditional robotic arms, providing increased safety for human-robot interaction, and increasing the range of physical human assistance tasks that can be undertaken by autonomous systems.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.