Purpose

For efficient trajectory control of industrial robots, a cumbersome computation for inverse kinematics and inverse dynamics is needed, which is usually developed using spatial transformation using Denavit–Hartenberg principle and Lagrangian or Newton–Euler methods, respectively. The model is highly non-linear and needs to deal with uncertainties because of lack of accurate measurement of mechanical parameters, noise and non-inclusion of joint friction, which results in some inaccuracies in predicting accurate torque trajectories. To get a guaranteed closed form solution, the robot designers normally follow Pieper’s recommendation and compromise with the mechanical design. While this may be acceptable for the industrial robots where the aesthetic look is not that important, it is not for humanoid and social robots. To help solve this problem, this study aims to propose an alternative machine learning-based computational approach based on a multi-gated sequence model for finding appropriate mapping between Cartesian space to joint space and motion space to joint torque space.

Design/methodology/approach

First, the authors generate sufficient data required for the sequence model, using forward kinematics and forward dynamics by running N number of nested loops, where N is the number of joints of the robot. Subsequently, to develop a learning-based model based on sequence analysis, the authors propose to use long short-term memory (LSTM) and hence, train an LSTM model, the architecture details of which have been discussed in the paper. To make LSTM learning algorithms perform efficiently, the authors need to detect and eliminate redundant features from the data set, which the authors propose to do using an elegant statistical tool called Pearson coefficient.

Findings

To validate the proposed model, the authors have performed rigorous experiments using both hardware and simulation robots (Baxter/Anukul robot) available in their laboratory and KUKA simulation robot data set made available from Neural Learning for Robotics Laboratory. Through several characteristic plots, it has been shown that a sequence-based LSTM model of deep learning architecture with non-redundant features could help the robots to learn smooth and accurate trajectories more quickly compared to data sets having redundancy. Such data-driven modeling techniques can change the future course of direction of robotics research for solving the classical problems such as trajectory planning and motion planning for manipulating industrial as well as social humanoid robots.

Originality/value

The present investigation involves development of deep learning-based computation model, statistical analyses to eliminate redundant features, data creation from one hardware robot (Anukul) and one simulation robot model (KUKA), rigorously training and testing separately two computational models (specially configured two LSTM models) – one for learning inverse kinematics and one for learning inverse dynamics problem – and comparison of the inverse dynamics model with the state-of-the-art model. Hence, the authors strongly believe that the present paper is compact and complete to get published in a reputed journal so that dissemination of new ideas can benefit the researchers in the area of robotics.

中文翻译：

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多门控序列模型对工业机器人运动学和动力学学习的有效性

目的

为了对工业机器人进行有效的轨迹控制，需要进行繁琐的逆运动学和逆动力学计算，这通常是分别使用Denavit-Hartenberg原理和Lagrangian或Newton-Euler方法通过空间变换来开发的。该模型是高度非线性的，并且由于缺乏对机械参数的准确测量，噪声以及不包含关节摩擦而需要处理不确定性，这导致在预测准确的转矩轨迹方面存在一些误差。为了获得有保证的封闭式解决方案，机器人设计人员通常会遵循Pieper的建议，并在机械设计上做出妥协。虽然这对于外观不是那么重要的工业机器人是可以接受的，但对于类人机器人和社交机器人却不是。为了解决这个问题，

设计/方法/方法

首先，作者通过运行N个嵌套循环（其中N是机器人的关节数），使用正向运动学和正向动力学来生成序列模型所需的足够数据。随后，为了开发基于序列分析的基于学习的模型，作者建议使用长短期记忆（LSTM），从而训练LSTM模型，该模型的体系结构详细信息已在本文中进行了讨论。为了使LSTM学习算法高效地执行，作者需要从数据集中检测并消除冗余特征，作者建议使用一种称为Pearson系数的优雅统计工具来做到这一点。

发现

为了验证提出的模型，作者使用了实验室中可用的硬件和模拟机器人（Baxter / Anukul机器人）以及可从机器人实验室神经学习中获得的KUKA模拟机器人数据集进行了严格的实验。通过几个特征图，已经表明，与具有冗余的数据集相比，具有非冗余特征的基于序列的深度学习体系结构的LSTM模型可以帮助机器人更快地学习平滑且准确的轨迹。这种数据驱动的建模技术可以改变机器人研究的未来方向，以解决诸如操纵工业和社会人形机器人的轨迹规划和运动规划之类的经典问题。

创意/价值

本研究涉及开发基于深度学习的计算模型，消除冗余功能的统计分析，从一个硬件机器人（Anukul）和一个仿真机器人模型（KUKA）创建数据，分别严格训练和测试两个计算模型（特别配置两个LSTM模型）–一种用于学习逆运动学，一种用于学习逆动力学问题–并将逆动力学模型与最新模型进行比较。因此，作者坚信本论文是紧凑而完整的，可以在知名杂志上发表，因此新思想的传播可以使机器人技术领域的研究人员受益。