When a robot performs a task next to a human, physical interaction is
inevitable: the human might push, pull, twist, or guide the robot. The
state-of-the-art treats these interactions as disturbances that the robot
should reject or avoid. At best, these robots respond safely while the human
interacts; but after the human lets go, these robots simply return to their
original behavior. We recognize that physical human-robot interaction (pHRI) is
often intentional -- the human intervenes on purpose because the robot is not
doing the task correctly. In this paper, we argue that when pHRI is intentional
it is also informative: the robot can leverage interactions to learn how it
should complete the rest of its current task even after the person lets go. We
formalize pHRI as a dynamical system, where the human has in mind an objective
function they want the robot to optimize, but the robot does not get direct
access to the parameters of this objective -- they are internal to the human.
Within our proposed framework human interactions become observations about the
true objective. We introduce approximations to learn from and respond to pHRI
in real-time. We recognize that not all human corrections are perfect: often
users interact with the robot noisily, and so we improve the efficiency of
robot learning from pHRI by reducing unintended learning. Finally, we conduct
simulations and user studies on a robotic manipulator to compare our proposed
approach to the state-of-the-art. Our results indicate that learning from pHRI
leads to better task performance and improved human satisfaction.

本论文旨在提出了一种动态系统的人机物理互动框架，介绍了如何在实时响应中学习人类意图，优化任务，并提高用户满意度。

通过物理交互实现沟通：从人类校正中在线学习机器人目标

Physical Interaction as Communication: Learning Robot Objectives Online  from Human Corrections

Keyword spotting (KWS) is a critical component for enabling speech based user
interactions on smart devices. It requires real-time response and high accuracy
for good user experience. Recently, neural networks have become an attractive
choice for KWS architecture because of their superior accuracy compared to
traditional speech processing algorithms. Due to its always-on nature, KWS
application has highly constrained power budget and typically runs on tiny
microcontrollers with limited memory and compute capability. The design of
neural network architecture for KWS must consider these constraints. In this
work, we perform neural network architecture evaluation and exploration for
running KWS on resource-constrained microcontrollers. We train various neural
network architectures for keyword spotting published in literature to compare
their accuracy and memory/compute requirements. We show that it is possible to
optimize these neural network architectures to fit within the memory and
compute constraints of microcontrollers without sacrificing accuracy. We
further explore the depthwise separable convolutional neural network (DS-CNN)
and compare it against other neural network architectures. DS-CNN achieves an
accuracy of 95.4%, which is ~10% higher than the DNN model with similar number
of parameters.

本研究旨在针对资源受限的微控制器上运行的关键词检测（Keyword spotting，KWS）应用，对神经网络架构进行评估和探索。我们训练了各种已经在文献中发表的用于关键词检测的神经网络架构，比较了它们的准确性和存储器 / 计算要求。我们表明，我们可以优化这些神经网络架构来适应微控制器的存储器和计算限制，而不会牺牲准确性。此外，我们进一步探索了深度可分离卷积神经网络（DS-CNN），并将其与其他神经网络架构进行了比较。DS-CNN 实现了 95.4％的准确性，比具有类似参数数量的 DNN 模型高出约 10％。