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

基本信息

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

项目摘要

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.

一些决策问题，例如加载洗碗机或组装家具，需要明确规划一系列步骤，而另一些则需要通过学习的反射来大部分进行，例如抓住物体或转弯以避免停车的汽车。在机器人技术中，仍然不太了解为什么有些问题需要计划，以及为什么可以通过反射解决其他问题。该项目探讨了如何应用数学分支的技术如何研究机器人决策问题的基本性质。通过开发应用拓扑来优化，分析和可视化机器人动作数据集的算法，研究人员希望更好地了解计划和学习之间的灰色区域。最终，这种更好的理解可以帮助其他工程师发展更快，有能力和强大的机器人行为。这项研究的技术目标是分析将机器人决策问题与解决方案联系起来的数学关系，以阐明围绕运动计划和控制学习之间联系的基本问题。从开发一种解决个体问题查询的算法的经典规划范式中，该项目研究了相关问题实例连续变化的全球拓扑特征。具体而言，它研究了拓扑复杂性的某些特征与学习和计划算法的性能如何相关。基于这种理解，开发了新的算法，拓扑指标和可视化技术，以帮助利用拓扑结构来更快地计划和更准确的学习。对冗余逆运动，腿部机器人和敏捷自动驾驶汽车的基准问题进行了评估。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

Time-Optimal Trajectory Generation for Dynamic Vehicles: A Bilevel Optimization Approach

DOI：
10.1109/iros40897.2019.8968104
发表时间：
2019-01-01
期刊：
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
影响因子：
0
作者：
Tang, Gao;Sun, Weidong;Hauser, Kris
通讯作者：
Hauser, Kris

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)
影响因子：
0
作者：
Gao Tang;Weidong Sun;Kris K. Hauser
通讯作者：
Gao Tang;Weidong Sun;Kris K. Hauser

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