RI: Small: Exploiting Global Structure in Robot Decision Problems

RI：小：在机器人决策问题中利用全局结构

• 批准号: 1816540
• 负责人: Kris Hauser
• 金额: $ 34.88万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816540&HistoricalAwards=false
• 关键词: RI; Small; Exploiting; Global; Structure

Some decision problems, like loading a dishwasher or assembling furniture, require explicit planning about a sequence of steps, while others, like grasping an object or swerving to avoid a stopped car, can be performed largely through learned reflexes. In robotics, it is still poorly understood why some problems require planning and why others can be solved via reflex. This project explores how techniques from topology, a branch of mathematics, can be applied to study the fundamental nature of robot decision problems. By developing algorithms that apply topology to optimize, analyze, and visualize large datasets of robot motions, the researchers hope to better understand the gray area between planning and learning. Ultimately, this better understanding could help other engineers develop more responsive, capable, and robust robot behaviors. The technical goal of this research is to analyze the mathematical relation connecting robot decision problems to their solutions in order to shed light on fundamental questions surrounding the connection between motion planning and control learning. Breaking from the classical planning paradigm of developing an algorithm that solves individual problem queries, the project studies the global topological characteristics of continuous variations of related problem instances. Specifically, it investigates how certain features of topological complexity relate to the performance of learning and planning algorithms. Based on this understanding, new algorithms, topological metrics, and visualization techniques are developed to help exploit topological structure for faster planning and more accurate learning. The proposed methods are evaluated on benchmark problems in redundant inverse kinematics, legged robots, and agile autonomous vehicles.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.

有些决策问题，如装洗碗机或组装家具，需要对一系列步骤进行明确的规划，而其他问题，如抓住一个物体或转向以避开一辆停在路边的汽车，则主要可以通过习得反射来完成。在机器人领域，人们仍然不太理解为什么有些问题需要计划，而另一些问题可以通过反射来解决。这个项目探索如何从拓扑学的技术，数学的一个分支，可以应用于研究机器人决策问题的基本性质。通过开发应用拓扑优化、分析和可视化机器人运动大数据集的算法，研究人员希望更好地理解规划和学习之间的灰色地带。最终，这种更好的理解可以帮助其他工程师开发出更灵敏、更有能力、更健壮的机器人行为。本研究的技术目标是分析机器人决策问题与其解决方案之间的数学关系，以阐明围绕运动规划与控制学习之间联系的基本问题。该项目打破了开发解决单个问题查询的算法的经典规划范式，研究了相关问题实例连续变化的全局拓扑特征。具体来说，它研究了拓扑复杂性的某些特征与学习和规划算法的性能之间的关系。基于这种理解，新的算法、拓扑度量和可视化技术被开发出来，以帮助利用拓扑结构进行更快的规划和更准确的学习。在冗余逆运动学、腿式机器人和敏捷自动驾驶汽车的基准问题上对所提出的方法进行了评估。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

A data-driven indirect method for nonlinear optimal control

• DOI: 10.1007/s42064-019-0051-3
• 发表时间: 2019-12-01
• 期刊: ASTRODYNAMICS
• 影响因子: 6.1
• 作者: Tang, Gao;Hauser, Kris
• 通讯作者: Hauser, Kris

Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts

• DOI: 10.1109/icra.2019.8793909
• 发表时间: 2018-03
• 期刊: 2019 International Conference on Robotics and Automation (ICRA)
• 作者: Gao Tang;Kris K. Hauser

Enhancing Bilevel Optimization for UAV Time-Optimal Trajectory using a Duality Gap Approach

• DOI: 10.1109/icra40945.2020.9196789
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Gao Tang;Weidong Sun;Kris K. Hauser

Learning Trajectories for Real- Time Optimal Control of Quadrotors

• DOI: 10.1109/iros.2018.8593536
• 发表时间: 2018-10
• 期刊: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Gao Tang;Weidong Sun;Kris K. Hauser