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RTML: Large: Co-design of Hardware and Algorithms for Energy-efficient Robot Learning

RTML：大型：节能机器人学习的硬件和算法协同设计

基本信息

批准号：
1937501
负责人：
Sertac Karaman
金额：
$ 100万
依托单位：
Massachusetts Institute of Technology
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：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937501&HistoricalAwards=false
关键词：
RTML Large Co design Hardware

项目摘要

Miniature low-energy autonomous robotic vehicles, ranging from insect-size flyers to palm- size satellites, hold the potential for tremendous impact in a diverse set of industries, including consumer electronics, high-bandwidth communications, search and rescue operations, and space exploration, just to name a few. Next-generation low-energy computing hardware that will enable these applications must be adaptable, i.e., recognizing new environments on the fly, learning their characteristic features in real time, and adapting its computing strategy to minimize the energy consumption required for computing task. This project will help realize vehicles that are able to improve the accuracy of their perception and decision making algorithms, simply by experimenting with obtaining a diverse set of viewpoints of the environment and utilizing the knowledge of its motion to ground and improve its observation via machine learning. The project also seeks to develop new graduate and undergraduate courses at MIT, it will enable outreach for high school students, involve women and underrepresented groups, thus helping train the future US workforce. This project will develop real-time robot learning algorithms and hardware focusing on three core areas. Firstly, the project will develop real-time continuous robot learning systems that improve performance of robot perception and decision making by rapid learning in new environments. Secondly, the project will develop real-time active robot learning systems to efficiently decide the balance between improving accuracy of perception and decision making algorithms and focusing on accomplishing the task at hand. Thirdly, the project will develop real-time adaptable robot learning systems for energy scalable perception and decision making, where the design allows for efficient accuracy-energy tradeoffs. The project will help develop new hardware and algorithms for real-time robot learning, by enabling new low-energy robotic systems. The project will also collaborate with a synergistic DARPA program for related hardware development.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.

微型低能耗自主机器人车辆，从昆虫大小的飞行器到手掌大小的卫星，在各种行业中具有巨大影响的潜力，包括消费电子，高带宽通信，搜索和救援行动以及太空探索，仅举几例。支持这些应用的下一代低能耗计算硬件必须具有适应性，即，在运行中识别新的环境，真实的时间学习它们的特性特征，并调整其计算策略以最小化计算任务所需的能量消耗。该项目将有助于实现能够提高其感知和决策算法准确性的车辆，只需通过实验获得环境的各种观点，并利用其运动知识，通过机器学习改善其观察。该项目还寻求在麻省理工学院开发新的研究生和本科生课程，它将为高中生提供外展服务，让妇女和代表性不足的群体参与进来，从而帮助培训未来的美国劳动力。该项目将开发实时机器人学习算法和硬件，重点关注三个核心领域。首先，该项目将开发实时连续机器人学习系统，通过在新环境中的快速学习来提高机器人感知和决策的性能。其次，该项目将开发实时主动机器人学习系统，以有效地决定提高感知和决策算法的准确性与专注于完成手头任务之间的平衡。第三，该项目将开发实时自适应机器人学习系统，用于能量可扩展的感知和决策，其中设计允许有效的准确性-能量权衡。该项目将通过启用新的低能耗机器人系统，帮助开发用于实时机器人学习的新硬件和算法。该项目还将与DARPA的一个协同项目合作，进行相关的硬件开发。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进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Uncertainty from Motion for DNN Monocular Depth Estimation

DOI：
10.1109/icra46639.2022.9812222
发表时间：
2022-05
期刊：
2022 International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Soumya Sudhakar;V. Sze;S. Karaman
通讯作者：
Soumya Sudhakar;V. Sze;S. Karaman

Data Centers on Wheels: Emissions From Computing Onboard Autonomous Vehicles

DOI：
10.1109/mm.2022.3219803
发表时间：
2023-01
期刊：
IEEE Micro
影响因子：
3.6
作者：
Soumya Sudhakar;V. Sze;S. Karaman
通讯作者：
Soumya Sudhakar;V. Sze;S. Karaman

Efficient Computation of Map-scale Continuous Mutual Information on Chip in Real Time

芯片上地图尺度连续互信息的实时高效计算

DOI：
10.1109/iros51168.2021.9636603
发表时间：
2021
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
影响因子：
0
作者：
Gupta, Keshav;Li, Peter Zhi;Karaman, Sertac;Sze, Vivienne
通讯作者：
Sze, Vivienne

Memory-Efficient Gaussian Fitting for Depth Images in Real Time

实时深度图像的内存高效高斯拟合