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

技术进步导致机器人使用机器学习来协助人类完成各种任务。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，使它们容易受到对手的利用。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非常巨大。 或者，将联合收割机（不太复杂）深度学习与其他概率技术相结合的混合方法可以提供更鲁棒和自适应的学习。 不幸的是，这些概率技术往往遭受长运行时间和高计算复杂性。 该项目旨在开发一系列机器人应用中这些概率技术的硬件加速新方法。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感知，感知和真实的时间行动。这项拟议中的工作有可能通过使机器人能够显着扩大它们可以完成的任务范围来提高人类的生活质量。 该项目还包括机器人设计跨学科课程的课程开发，其部分目的是让更广泛的学生对机器人系统的计算和硬件设计感兴趣。长期目标是使移动的机器人能够在真实的时间内计算感知所需的所有信息。该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，以真实的时间和有效的能源成本在非结构化的自然环境中提供有效的机器人感知。特别是，该研究的目的是在有限的硬件和电源预算下，在有限的嵌入式系统内的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。该项目提出三项主要任务：B）构建用于加速面向机器人的算法的优化硬件模块的通用库，（c）利用硬件库开发机器人感知的新算法。该奖项反映了NSF的法定使命，并通过利用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.