A Deep Learning Approach to Navigating the Joint Solution Space of Redundant Inverse Kinematics and Its Applications to Numerical IK Computations

As an increasing number of robotic manipulators possess seven or more degrees-of-freedom (DoF), solving inverse kinematic (IK) for kinematically redundant manipulators is becoming critical. Numerical optimizations are commonly used to solve the problem due to their generality and accuracy. Unfortuna...

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Published in:	IEEE access Vol. 11; pp. 2274 - 2290
Main Authors:	Ho, Chi-Kai, Chan, Li-Wei, King, Chung-Ta, Yen, Ting-Yu
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Deep learning Degrees of freedom End effectors Feature encoding Inverse kinematics Iterative methods Kinematics Manipulators Neural networks Numerical methods Numerical models Obstacle avoidance Optimization Redundancy redundant robotic manipulators Robot arms Robots Solution space Unsupervised learning
ISSN:	2169-3536, 2169-3536
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Abstract	As an increasing number of robotic manipulators possess seven or more degrees-of-freedom (DoF), solving inverse kinematic (IK) for kinematically redundant manipulators is becoming critical. Numerical optimizations are commonly used to solve the problem due to their generality and accuracy. Unfortunately, they typically only generate one joint solution at a time, despite the multiple joint configurations that redundant manipulators can provide to move the end-effector to a target position. The long iterative optimization process is also a concern, particularly if extra constraints such as obstacle avoidance have to be evaluated. In this paper, we show that numerical methods may be complemented by deep learning to overcome these limitations. Through deep learning, the solution space of redundant IK may be learned with neural networks (NNs), which allows multiple distinct joint solutions corresponding to a given target position to be obtained by navigating the solution space. The main challenge is to overcome the one-to-one functional mapping of NNs. This paper solves this problem with a novel probabilistic encoding of manipulator poses and their corresponding infinite number of joint solutions. Two examples are presented to demonstrate the application of the proposed method to facilitate numerical IK computations: (1) finding a good initial joint solution to bootstrap the numerical IK calculation, and (2) evaluating extra constraints, such as obstacle avoidance, off the optimization iterations. Experiments show that the proposed method can accelerate the execution of different numerical IK modules in the popular IKpy package up to 50% for a 7-DoF manipulator, depending on the accuracy required.
AbstractList	As an increasing number of robotic manipulators possess seven or more degrees-of-freedom (DoF), solving inverse kinematic (IK) for kinematically redundant manipulators is becoming critical. Numerical optimizations are commonly used to solve the problem due to their generality and accuracy. Unfortunately, they typically only generate one joint solution at a time, despite the multiple joint configurations that redundant manipulators can provide to move the end-effector to a target position. The long iterative optimization process is also a concern, particularly if extra constraints such as obstacle avoidance have to be evaluated. In this paper, we show that numerical methods may be complemented by deep learning to overcome these limitations. Through deep learning, the solution space of redundant IK may be learned with neural networks (NNs), which allows multiple distinct joint solutions corresponding to a given target position to be obtained by navigating the solution space. The main challenge is to overcome the one-to-one functional mapping of NNs. This paper solves this problem with a novel probabilistic encoding of manipulator poses and their corresponding infinite number of joint solutions. Two examples are presented to demonstrate the application of the proposed method to facilitate numerical IK computations: (1) finding a good initial joint solution to bootstrap the numerical IK calculation, and (2) evaluating extra constraints, such as obstacle avoidance, off the optimization iterations. Experiments show that the proposed method can accelerate the execution of different numerical IK modules in the popular IKpy package up to 50% for a 7-DoF manipulator, depending on the accuracy required.
Author	Chan, Li-Wei King, Chung-Ta Ho, Chi-Kai Yen, Ting-Yu
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References	ref13 ref35 ref34 ref15 ref37 ref14 ref36 ref31 ref30 ref11 DeMers (ref12); 4 ref33 ref10 ref32 ref2 ref1 Kraft (ref22) 1988 ref39 ref16 ref19 ref18 Lu (ref26) 2018 Razavi (ref38); 32 LaValle (ref24) 1998 ref46 ref23 ref45 ref25 Coumans (ref8) 2016 ref20 ref42 ref41 ref44 ref21 ref43 ref27 Manceron (ref28) 2022 ref29 ref7 ref9 ref4 Sohn (ref40); 28 ref3 ref6 ref5 Ghasemi (ref17) 2019
References_xml	– ident: ref10 doi: 10.1109/M2VIP.2017.8211457 – ident: ref4 doi: 10.1109/ROBOT.1989.99999 – ident: ref16 doi: 10.1002/9781118723203 – ident: ref15 doi: 10.1016/j.protcy.2013.12.451 – ident: ref27 doi: 10.1109/ROBOT.1993.291835 – ident: ref14 doi: 10.1109/IROS.2001.973374 – ident: ref43 doi: 10.1109/70.86079 – year: 1998 ident: ref24 article-title: Rapidly-exploring random trees: A new tool for path planning – ident: ref31 doi: 10.1163/016918609X12529299964101 – ident: ref3 doi: 10.1109/IROS.2003.1248841 – ident: ref11 doi: 10.1109/FUZZ-IEEE.2019.8858872 – ident: ref18 doi: 10.1145/3422622 – ident: ref19 doi: 10.1109/70.508439 – volume: 28 start-page: 3483 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref40 article-title: Learning structured output representation using deep conditional generative models – ident: ref13 doi: 10.1109/ROBOT.1993.291984 – volume-title: IKPy: An Inverse Kinematics Library Aiming Performance and Modularity (V3.3.3) year: 2022 ident: ref28 – ident: ref36 doi: 10.1007/978-3-030-29743-5_23 – ident: ref41 doi: 10.1016/j.cie.2020.106682 – ident: ref5 doi: 10.1080/2151237X.2005.10129202 – volume: 32 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref38 article-title: Generating diverse high-fidelity images with VQ-VAE-2 – ident: ref7 doi: 10.1007/978-3-319-32552-1_10 – ident: ref34 doi: 10.1109/TETCI.2020.2991774 – ident: ref6 doi: 10.1137/0916069 – year: 2016 ident: ref8 article-title: Pybullet, a Python module for physics simulation for games, robotics and machine learning – ident: ref29 doi: 10.1109/IROS40897.2019.8968605 – ident: ref21 doi: 10.1109/TPAMI.2020.2992934 – ident: ref20 doi: 10.48550/arXiv.1312.6114 – ident: ref35 doi: 10.1016/j.mechmachtheory.2018.05.013 – ident: ref25 doi: 10.1017/S0263574700001430 – ident: ref42 doi: 10.1109/TSMC.1986.289285 – ident: ref9 doi: 10.1109/ICARCV.2012.6485351 – year: 2018 ident: ref26 article-title: Anomaly detection for skin disease images using variational autoencoder publication-title: arXiv:1807.01349 – volume: 4 start-page: 589 volume-title: Proc. 4th Int. Conf. Neural Inf. Process. Syst. ident: ref12 article-title: Learning global direct inverse kinematics – volume-title: Software Package for Sequential Quadratic Programming year: 1988 ident: ref22 – ident: ref30 doi: 10.1115/1.3143764 – ident: ref39 doi: 10.1007/BF00126069 – ident: ref45 doi: 10.1109/TNNLS.2019.2900734 – ident: ref1 doi: 10.1155/2016/5720163 – ident: ref32 doi: 10.1109/ROBOT.2001.932681 – ident: ref46 doi: 10.1016/j.mechmachtheory.2017.12.005 – ident: ref23 doi: 10.1109/ROBOT.1990.126090 – ident: ref2 doi: 10.1016/j.gmod.2011.05.003 – ident: ref44 doi: 10.1109/3477.907574 – ident: ref37 doi: 10.1109/IRDS.2002.1044034 – year: 2019 ident: ref17 article-title: Kinematic synthesis of parallel manipulator via neural network approach publication-title: arXiv:1904.04668 – ident: ref33 doi: 10.15302/J-ENG-2015009
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SubjectTerms	Artificial neural networks Deep learning Degrees of freedom End effectors Feature encoding Inverse kinematics Iterative methods Kinematics Manipulators Neural networks Numerical methods Numerical models Obstacle avoidance Optimization Redundancy redundant robotic manipulators Robot arms Robots Solution space Unsupervised learning
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Title	A Deep Learning Approach to Navigating the Joint Solution Space of Redundant Inverse Kinematics and Its Applications to Numerical IK Computations
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