SHF: Core: Small: Real-time and Energy-Efficient Machine Learning for Robotics Applications

技术的进步导致使用机器学习来协助人类完成各种任务的机器人激增。然而，尽管基于深度学习的方法具有优势，但它们也有一些缺点，使它们容易被对手利用。此外，训练这些判别模型所产生的计算、财务和环境成本可能相当巨大。或者，将（不太复杂的）深度学习与其他概率技术相结合的混合方法可以提供更强大和自适应的学习。不幸的是，这些概率技术往往存在运行时间长和计算复杂度高的问题。该项目旨在为这些概率技术在一系列机器人应用中的硬件加速开发新的方法。这些方法旨在为自主机器人的设计铺平道路，这些机器人可以在一系列自然人类环境中以非常节能的方式实时感知、感知和行动。这项提议的工作有可能提高人类的生活质量，使机器人能够大大扩大它们可以完成的任务范围。该项目还包括机器人设计跨学科课程的课程开发，部分目的是让更多的学生对机器人系统的计算和硬件设计感兴趣。一个长期目标是使移动机器人能够实时计算所有感知所需的信息。这个项目的重点是利用深度学习和概率推理的互补特性来做出感知决策，其中一个的弱点可以通过另一个的优势来解决。研究人员正在研究各种算法和硬件加速方法，以有效的能源成本在非结构化的自然环境中实时提供有效的机器人感知。特别是，该研究旨在在有限的硬件和功率预算下，在受限的嵌入式系统中进行目标导向的机器人操作。重点是概率算法，如贝叶斯推理，可以与神经网络方法相结合。该项目提出了三个主要任务：a)加速硬件中基于图的贝叶斯推理，b)构建优化硬件模块的通用库，用于加速面向机器人的算法，以及c)使用硬件库开发用于机器人感知的新算法。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。

Technological advancements have led to a proliferation of robots using machine learning to assist humans in a wide range of tasks. However, despite the strengths of approaches based on deep learning they have several shortcomings that leave them vulnerable to exploitation from adversaries. In addition, the computational, financial, and environmental cost incurred to train these discriminative models can be quite immense. Alternatively, hybrid methods that combine (less complex) deep learning with other probabilistic techniques can provide more robust and adaptive learning. Unfortunately, these probabilistic techniques tend to suffer from long run times and high computational complexity. This project aims to develop new approaches for hardware acceleration of these probabilistic techniques across a range of robotics applications. These approaches are intended to pave the way for the design of autonomous robots that can sense, perceive, and act in real time in a range of natural human environments, and in a very energy-efficient manner. The proposed work has the potential to enhance human quality-of-life by enabling robots to dramatically expand the range of tasks they can complete. The project also includes curriculum development for an interdisciplinary course in robotic design aimed in part at getting a broader range of students interested in computing and hardware design of robotic systems.A long-term goal is to reach the point where mobile robots can compute all information needed for perception on-board and in real time. This project focuses on exploiting the complementary properties of deep learning and probabilistic inference for making perceptual decisions, where the weaknesses of one can be addressed by the strengths of the other. The researchers are investigating various algorithmic and hardware-acceleration approaches that provide effective robot perception in unstructured, natural environments in real time and at efficient energy cost. In particular, the research is aimed at goal-directed robot manipulation within a confined embedded system under limited hardware and power budgets. The focus is on probabilistic algorithms such as Bayesian inference that may be incorporated with neural network methods. The project proposes three main tasks: a) accelerating graph-based Bayesian inference in hardware, b) constructing a general-purpose library of optimized hardware modules for accelerating robot-oriented algorithms, and c) using the hardware library to develop new algorithms for robot perception.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.