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

NRI：FND：COLLAB：动态多行为机器人的设计：考虑设计权衡并实现功能更强大的机器人系统的新工具

• 批准号: 1924723
• 负责人: Aaron Johnson
• 金额: $ 25万
• 依托单位: Carnegie-Mellon 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: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924723&HistoricalAwards=false
• 关键词: NRI; FND; COLLAB; Design; dynamic

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.

该项目将为设计执行多种动态行为的机器人创造新技术。目前，大多数机器人只能执行一组有限的行为，比如拾取和放置物体，或者在平地上行走和奔跑。为了扩展机器人的行为技能，标准做法是简单地将现有的机器人组合起来；例如，将机器人手臂安装在轮式或腿式机器人基座上，就能制造出既能移动又能拾取物体的机器人。这种设计方法是权宜之计，但有明显的缺点：首先，最终的设计可能不切实际或昂贵；其次，可以组合的独立机器人的数量是有限的，这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，一条肢体可以同时用作腿和手臂，这在动物王国中很常见——但这样的机器人更难制造，因为设计和行为之间的关系很复杂。多行为设计的新范式将产生能够在广泛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工作，因此它们必须具有上楼、清理杂物、翻抽屉、操作小物体等方面的灵活性。该项目的结果可能使机器能够根据所需的特定行为集的需求进行定制，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究和开发的进入门槛，使复杂机器人的设计更容易，向工程师和企业家开放这个领域，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构建具有多种行为的系统，该项目寻求创建自动化技术，可以重用不同行为中的部件，同时推理用例之间出现的性能权衡。为了实现这一目标，该项目将使用基于物理的降阶模型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将允许分析和综合多行为机器人，暴露竞争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师在行为性能的空间中工作，而不必独立考虑每个设计参数，从而大大减少搜索空间。这些方法将使机器人的设计能够根据任务场景进行定制，从而提高整个系统的效率和有效性。这些结果将在一个案例研究中进行演示和评估，其中商用四足机器人的设计是定制的，能够执行多种动态行为，以在不同的场景中执行抓取任务。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。

项目成果

The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems

• DOI: 10.1016/j.automatica.2021.109752
• 发表时间: 2021-09
• 期刊: Autom.
• 作者: Nathan J. Kong;J. Payne;George Council;Aaron M. Johnson

Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots

• DOI: 10.1109/tro.2023.3308773
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Nathan J. Kong;Chuanzheng Li;George Council;Aaron M. Johnson

iLQR for Piecewise-Smooth Hybrid Dynamical Systems

用于分段平滑混合动力系统的 iLQR

• DOI: 10.1109/cdc45484.2021.9683506
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Kong, Nathan J.;Council, George;Johnson, Aaron M.
• 通讯作者: Johnson, Aaron M.

Proprioception and Reaction for Walking Among Entanglements

在纠缠中行走的本体感觉和反应

• DOI: 10.1109/iros55552.2023.10341986
• 发表时间: 2023
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems
• 作者: Yim, Justin K.;Ren, Jiming;Ologan, David;Gonzalez, Selvin Garcia;Johnson, Aaron M.
• 通讯作者: Johnson, Aaron M.

Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits

混合事件整形以稳定周期性混合轨道

• 发表时间: 2022
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Zhu, James;Kong, Nathan J.;Council, George;Johnson, Aaron M.
• 通讯作者: Johnson, Aaron M.