NRI: FND: COLLAB: Design of dynamic multibehavioral robots: new tools to consider design tradeoff and enable more capable robotic systems

这个项目将为设计执行多种动态行为的机器人创造新的技术。目前，大多数机器人可以执行有限的一组行为，如拾取和放置物体或在平地上行走和奔跑。为了扩展机器人的行为能力，标准做法是将现有的机器人简单地组合在一起；例如，将机器人手臂连接到轮式或腿式机器人底座上，就可以产生既能四处移动又能拾取和放置物体的机器人。这种设计方法是便利的，但也有明显的缺点：第一，由此产生的设计可能过于庞大或昂贵；第二，可以组合的独立机器人的数量受到限制，限制了组合机器人的行为能力。对机器人来说，最大限度地重复利用现有部件会更好--例如，像动物王国中常见的那样，一条腿可以同时用作腿和手臂--但这样的机器人要难得多，因为设计和行为之间的关系很复杂。一种新的多行为设计范例将产生能够在广泛的应用中帮助社会的机器人。例如，家庭辅助机器人必须在为人类建造的环境中操作，因此它们必须具有灵活性，可以在杂乱的环境中上楼，在抽屉中挖掘，操纵小物体，等等。该项目的结果可能会使机器能够根据所需的特定行为集的要求进行定制，从而降低成本、尺寸和复杂性。这里开发的方法将有助于降低机器人研发的进入门槛，使复杂机器人的设计变得更容易，向工程师和企业家开放领域，他们可以扩大机器人技术的应用范围。为了使机器人设计人员能够建立能够多行为的系统，该项目寻求创建自动化技术，该技术可以重复使用不同行为中的部件，同时推理用例之间出现的性能权衡。为了实现这一点，该项目将使用基于物理的降阶模型来分析动态机器人的设计和行为之间的关系。这些模型将捕获感兴趣的行为，并降低设计搜索空间的复杂性。这些关系的局部几何关系将允许分析和综合多行为机器人，揭示相互竞争的设计目标之间的权衡。这种重新制定的多目标优化问题将允许设计者在行为性能空间中工作，而不必独立考虑每个设计参数，从而显著减少搜索空间。这些方法将允许机器人的设计根据任务场景进行定制，从而提高整体系统的效率和效力。这些结果将在一个案例研究中得到演示和评估，在该案例研究中，商业可用的四足机器人的设计被定制为能够在不同的场景下执行多种动态行为来执行取物任务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will create new techniques for designing robots that perform multiple dynamic behaviors. Currently, most robots can execute a limited set of behaviors like picking-and-placing objects or walking and running over level ground. To expand a robot's behavioral repertoire, it is standard practice to simply combine existing robots; for instance, attaching a robot arm to a wheeled or legged robot base produces a robot that can both move around and pick-and-place objects. This approach to design is expedient, but has obvious drawbacks: first, the resulting designs may be impractically large or expensive; second, there is a limit to the number of separate robots that can be combined, limiting the combined robot's behavioral repertoire. It would be better for robots to maximally re-using existing parts -- for instance, a single limb could be used both as a leg and an arm as is common in the animal kingdom -- but such robots are much harder to create because the relationship between design and behavior is complex. A new paradigm of design for multi-behavior would produce robots that can help society in a wide range of applications. For instance, home assistance robots must operate in environments built for humans, and as such they must have the flexibility to travel upstairs, over clutter, dig through drawers, manipulate small objects, and more. The results of this project may enable machines that can be customized to the demands of the specific sets of behaviors needed, reducing cost, size, and complexity. The methods developed here will help to lower the barrier to entry for robotics research and development by making design of complex robots easier, opening the field to engineers and entrepreneurs who can expand the range of applications of robotic technology.To enable robot designers to build systems that are capable of multiple behaviors, this project seeks to create automated techniques that can re-use parts in different behaviors while reasoning about performance tradeoffs that emerge between use cases. To achieve this, the project will analyze the relationship between design and behavior for dynamic robots using physics-based reduced order models. These models will capture the behaviors of interest and reduce the complexity of the design search space. The local geometry of these relationships will allow for the analysis and synthesis of multibehavioral robots that exposes the tradeoffs between competing design objectives. This reformulated multiobjective optimization problem will allow a designer to work in the space of behavioral performance without having to consider each design parameter independently, resulting in a significantly reduced search space. These methods will allow for a robot's design to be customized to the task scenario, increasing the overall system efficiency and effectiveness. These results will be demonstrated and evaluated in a case study wherein the design of a commercially-available quadrupedal robot is customized to capably execute multiple dynamic behaviors to perform a fetching task in varied scenarios.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.