NRI: FND: Towards Scalable and Self-Aware Robotic Perception

NRI：FND：迈向可扩展和自我意识的机器人感知

基本信息

批准号：
1924937
负责人：
Nuno Vasconcelos
金额：
$ 75万
依托单位：
University of California-San Diego
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：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924937&HistoricalAwards=false
关键词：
NRI FND Towards Scalable Self

项目摘要

Robot vision systems should be fast, to enhance the reaction times of robots to events in the visual world, capable of solving multiple vision problems simultaneously, and aware of their limitations. These properties are critical for robotic safety and collaboration. Safety is enhanced by faster reaction times (e.g. a car faster to detect obstacles has more room to stop before hitting them) and self-awareness (e.g., a robot should choose to stop to operate in situations that it deems too hard to be successful in). Collaboration is enhanced by scalability (which allows co-robots to solve more problems and thus behave more like human collaborators) and self-awareness (which simplifies the division of tasks between humans and robots, or teams of robots, with different skills). However, these properties have not been the focus of computer vision research, which has mostly addressed the design of networks that solve single tasks, usually requiring heavy computation and relatively low frame rates, and simply attempt to process all examples without any consideration for how difficult they are. This project addresses all these challenges, laying the foundation for a new generation of robotic perception systems that are more efficient, scalable, and self-aware. The research has applicability in areas of societal relevance, such as manufacturing, self-driving vehicles, intelligent systems, assisted living, homeland security, etc. Educationally, the project will provide exciting opportunities for both graduate and undergraduate research.This project pursues a research agenda composed of several integrated contributions that advance the state of the art in deep learning for robotic vison. This includes 1) novel neural network quantization techniques that address the quantization of both network weights and activations, leading to deep learning models that can be fully implemented with binary operations, significantly enhancing the speed of all AI computations; 2) new families of networks that exploit extensive parameter sharing to achieve scalable inference for task ecologies, substantially increasing the number of networks that can be cached in a processor and, therefore, the number of vision problems that can be solved simultaneously by a robot; 3) new network architectures for self-aware deep learning, capable of assessing the difficulty of each example, predicting failures, and refusing to process examples that are too difficult, so as to mitigate the possibility of catastrophic errors.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.

机器人视觉系统应该很快，以增强机器人对视觉世界中事件的反应时间，能够同时解决多个视力问题并意识到它们的局限性。这些特性对于机器人安全和协作至关重要。更快的反应时间可以提高安全性（例如，更快地检测障碍物的汽车在击中障碍物之前有更多的停止空间）和自我意识（例如，机器人应该选择停止以在很难成功的情况下停止操作）。可伸缩性（允许共同机器人可以解决更多问题，从而更像人类的合作者）和自我意识（这简化了人类与机器人之间的任务分裂，或者具有不同技能的机器人团队）来增强协作。但是，这些属性并不是计算机视觉研究的重点，它主要解决了解决单个任务的网络的设计，通常需要大量计算和相对较低的帧速率，并且只是尝试处理所有示例而无需考虑它们的困难。该项目应对所有这些挑战，为新一代的机器人感知系统奠定了基础，这些系统更有效，可扩展和自我意识。这项研究在社会相关性领域具有适用性，例如制造，自动驾驶汽车，智能系统，辅助生活，国土安全等。从教育上讲，该项目将为研究生和本科研究提供令人兴奋的机会。该项目由几个综合贡献组成，这些综合贡献促进了机器人葡萄酒的深度学习国家。这包括1）新的神经网络量化技术，这些技术涉及网络权重和激活的量化，从而导致可以通过二进制操作充分实现的深度学习模型，从而大大提高了所有AI计算的速度； 2）利用广泛参数共享以实现对任务生态的可扩展推断的新网络家族，大大增加了可以缓存在处理器中的网络数量，因此可以通过机器人同时解决的视力问题数量； 3）新的网络体系结构，用于自我意识深度学习，能够评估每个例子的难度，预测失败，并拒绝处理太困难的示例，以减轻灾难性错误的可能性。该奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的crietia crietia crietia crietia criter criter criter criter criter crietia crietia crietia crietia criter criTia criTia crietia crietia crietia crietia crietia crietia cromitia cromitia cromitia crietia crietia均值得一提。