Deep Learning-Driven Design of Robot Mechanisms
Deep Learning-Driven Design of Robot Mechanisms
复制标题
深度学习驱动的机器人机构设计
DOI：
10.1115/1.4062542
复制
发表时间：
2023
影响因子：
3.1
通讯作者：
Chakraborty, Nilanjan
中科院分区：
文献类型：
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作者：
Purwar, Anurag;Chakraborty, Nilanjan
在本文中，我们讨论了深度神经网络(DNN)的最新进展与机器人机构设计的收敛，这意味着将设计问题概念化为从设计规范空间到机构空间的参数化的学习问题。我们确定了三个关键的相互关联的问题，这些问题处于使用DNN的多功能性来解决机制设计问题的前沿。第一个问题是机构及其设计规范的表示问题，其中表示挑战主要来自数据的非欧几里得性质。第二个问题是建立从设计规范空间到机构的映射，理想情况下，我们希望为各种设计规范综合机构的类型和尺寸，包括路径综合、运动综合、枢轴位置约束等。第三个问题是为给定设计规范的端到端训练和多个候选机构的生成设计神经网络结构。我们还简要概述了这些问题中的每一个问题，并确定了研究界可能感兴趣的问题。
In this paper, we discuss the convergence of recent advances in deep neural networks (DNNs) with the design of robotic mechanisms, which entails the conceptualization of the design problem as a learning problem from the space of design specifications to a parameterization of the space of mechanisms. We identify three key inter-related problems that are at the forefront of using the versatility of DNNs in solving mechanism design problems. The first problem is that of representation of mechanisms and their design specifications, where the representation challenges arise primarily from the non-Euclidean nature of the data. The second problem is that of developing a mapping from the space of design specifications to the mechanisms where, ideally, we would like to synthesize both type and dimensions of the mechanism for a wide variety of design specifications including path synthesis, motion synthesis, constraints on pivot locations, etc. The third problem is that of designing the neural network architecture for end-to-end training and generation of multiple candidate mechanisms for a given design specification. We also present a brief overview of the state-of-the-art on each of these problems and identify questions of potential interest to the research community.