The challenge of teaching robots to perform dexterous manipulation, dynamic
locomotion, or whole--body manipulation from a small number of demonstrations
is an important research field that has attracted interest from across the
robotics community. In this work, we propose a novel approach by joining the
theories of Koopman Operators and Dynamic Movement Primitives to Learning from
Demonstration. Our approach, named \gls{admd}, projects nonlinear dynamical
systems into linear latent spaces such that a solution reproduces the desired
complex motion. Use of an autoencoder in our approach enables generalizability
and scalability, while the constraint to a linear system attains
interpretability. Our results are comparable to the Extended Dynamic Mode
Decomposition on the LASA Handwriting dataset but with training on only a small
fractions of the letters.

提出了一个新方法，通过结合 Koopman 操作符和动态运动原理与示教学习，将非线性动力系统投影到线性潜在空间中，从而实现复杂动作的解决方案。

基于 Koopman 的深度学习动态运动原理

Deep Learning for Koopman-based Dynamic Movement Primitives

This paper presents Stanford Doggo, a quasi-direct-drive quadruped capable of
dynamic locomotion. This robot matches or exceeds common performance metrics of
state-of-the-art legged robots. In terms of vertical jumping agility, a measure
of average vertical speed, Stanford Doggo matches the best performing animal
and surpasses the previous best robot by 22%. An overall design architecture is
presented with focus on our quasi-direct-drive design methodology. The hardware
and software to replicate this robot is open-source, requires only hand tools
for manufacturing and assembly, and costs less than $3000.

本文介绍了 Stanford Doggo，一种准直接驱动四足机器人，能够实现动态运动。该机器人在垂直跳跃敏捷度方面与最佳表现的动物相当或超越其之前最佳的机器人 22％。该机器人的综合设计架构是基于我们的准直接驱动设计方法，硬件和软件是开放源代码，并且制造和组装只需手工工具，成本不到 3000 美元。

Stanford Doggo: 一种开源的、准直接驱动四足机器人

Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.

本文提出了一种基于视觉反馈的动态着陆适应策略，该方法实现了快速适应脚着陆位置的连续调整，并使用卷积神经网络对自我监督的脚步进行分类，从而提高了机器人的整体运动性能。