CAREER: Computational Design for Robust Legged Robots

该项目旨在通过使工程设计直观并可通过计算实现机器人民主化。目前，机器人设计是一个时间和技能密集型的过程，需要多年的培训和多次迭代的原型设计和测试。释放无处不在的机器人的全部潜力需要新的智能设计工具，在设计过程中支持经验丰富的和新手用户，从构思到原型设计再到验证阶段。由此产生的方法将有可能彻底改变机器人设备的创建方式，提高人们设计和使用定制设备的能力，同时减少在这种定制上花费的时间。机器人系统新的灵活性和可用性将改善这些技术与日常社会的整合。专业工程师将获得对设计进行系统级分析和验证的能力，简化他们的工作，鼓励更多的创造力，并最终生产出更高效和更有效的机器。在教育方面，该项目将为下一代科学家和工程师提供直观的工具来探索工程概念。该项目将综合力学、计算几何、机器人技术以及数据聚类和压缩的思想，以创建一个用于设计、制造和控制腿式机器人的计算框架，该框架对任务、环境和制造的不确定性具有鲁棒性。虽然应用程序将是腿部运动，但工作将集中在计算框架和见解上，这些框架和见解可以推广到更广泛的机器人设计和定制领域。与静态物体不同，腿式机器人依赖于与周围环境的复杂交互来移动，因此它们的性能非常依赖于它们的物理设计。与现有的计算设计工具，专注于优化一个单一的指标相比，这项工作将因此评估的鲁棒性的建议的设计和它的适用性的一个实际的运动任务。该项目将产生1）模块化的，基于图形的表示机器人的组合几何，运动学和关节运动，2）新的分层数据聚类和压缩技术的语义表示和评估的设计，和3）算法的设计综合，提供可解释的设计的基础上评估的效率鲁棒性权衡。综合教育计划还汇集了工程和计算机科学的想法，将数值模拟和设计技术纳入本科和研究生课程，形成一个开源机器人设计社区，并鼓励更广泛地参与STEM，该奖项反映了NSF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。

This project aims to democratize robotics by making engineering design intuitive and accessible through computation. Currently robot design is a time and skill intensive process requiring years of training and multiple iterations of prototyping and testing. Unlocking the full potential of ubiquitous robotics requires new intelligent design tools that support experienced and novice users alike in the design process, from ideation to prototyping to validation stages. The resulting methods will have the potential to revolutionize the way robotic devices are created, increasing people's ability to design and use custom devices while reducing the time spent on this customization. New flexibility and availability of robotic systems will improve the integration of these technologies into everyday society. Professional engineers will gain the ability to perform systems-level analyses and verification of their designs, streamlining their work, encouraging more creativity, and ultimately producing more efficient and effective machines. In education, this project will inform next generation scientists and engineers by providing intuitive tools for exploring engineering concepts.The project will synthesize ideas from mechanics, computational geometry, robotics, and data clustering and compression to create a computational framework for designing, fabricating, and controlling legged robots that are robust to task, environment, and fabrication uncertainties. While the application will be legged locomotion, the work will focus on computational frameworks and insights that can be generalized to broader areas of robot design and customization. Unlike static objects, legged robots rely on complex interactions with their surrounding environment to locomote, and their performance is therefore very dependent on their physical designs. Compared with existing computational design tools, which focus on optimizing a single metric, this work will therefore evaluate the robustness of a proposed design and its suitability for a practical locomotion task. The project will produce 1) modular, graph-based representations for a robot's combined geometry, kinematics, and joint motion, 2) new hierarchical data clustering and compression techniques for semantic representation and evaluation of a design, and 3) algorithms for design synthesis that provide explainable designs based on evaluated efficiency-robustness tradeoffs. The integrated education plan also brings together ideas from engineering and computer science to incorporate numerical simulations and design techniques into courses at the undergraduate and graduate level, form an open-source robot design community, and encourage broader participation in STEM through outreach to K-12 students and teachers.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.