CLC number: TP242.6
On-line Access: 2023-01-21
Received: 2022-02-22
Revision Accepted: 2022-08-08
Crosschecked: 2023-01-21
Cited: 0
Clicked: 1829
Citations: Bibtex RefMan EndNote GB/T7714
Jiaqi WANG, Yongzhuo GAO, Dongmei WU, Wei DONG. Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200065 @article{title="Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization", %0 Journal Article TY - JOUR
针对下肢外骨骼机器人的基于概率运动基元的黑盒优化运动学习哈尔滨工业大学机器人技术与系统国家重点实验室,中国哈尔滨市,150001 摘要:外骨骼作为一种可穿戴的机器人,通过拟人化的构型直接传递机械动力来辅助或增强穿戴者运动。当外骨骼用于促进穿戴者的运动时,运动生成过程通常在高层控制中发挥重要作用。该领域的主要挑战之一是实时生成符合人类意图且可以适应不同情况的参考轨迹。在本文中,我们首先提出了一种基于概率运动基元(ProMP)的下肢外骨骼运动建模方法,它是一种用于生成运动轨迹的新型且强大的代表性工具。为了在不同穿戴者使用外骨骼时使轨迹适应不同情况,我们接着提出了一种基于黑盒优化PIBB结合ProMP的新型运动学习方案。运动模型首先由ProMP离线学习,它可以生成参考轨迹供外骨骼控制器在线使用,再采用PIBB在线学习和更新模型,提供了系统的自适应能力,消除了不确定性的影响。使用下肢外骨骼HEXO对六名受试者进行的模拟和实验证明了所提出方法的有效性。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Al-Shuka HFN, Corves B, Zhu WH, et al., 2016. Multi-level control of zero-moment point-based humanoid biped robots: a review. Robotica, 34(11):2440-2466. [2]Colombo G, Joerg M, Schreier R, et al., 2000. Treadmill training of paraplegic patients using a robotic orthosis. J Rehabil Res Dev, 37(6):693-700. [3]d’Avella A, Bizzi E, 2005. Shared and specific muscle synergies in natural motor behaviors. Proc Nat Acad Sci USA, 102(8):3076-3081. [4]Deng MD, Li ZJ, Kang Y, et al., 2020. A learning-based hierarchical control scheme for an exoskeleton robot in human-robot cooperative manipulation. IEEE Trans Cybern, 50(1):112-125. [5]Esquenazi A, Talaty M, Packel A, et al., 2012. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil, 91(11):911-921. [6]Fu CL, Chen K, 2008. Gait synthesis and sensory control of stair climbing for a humanoid robot. IEEE Trans Ind Electron, 55(5):2111-2120. [7]Guizzo E, Goldstein H, 2005. The rise of the body bots [robotic exoskeletons]. IEEE Spectr, 42(10):50-56. [8]Hassan M, Kadone H, Suzuki K, et al., 2014. Wearable gait measurement system with an instrumented cane for exoskeleton control. Sensors, 14(1):1705-1722. [9]He W, Li ZJ, Chen CLP, 2017. A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA J Autom Sin, 4(4):602-609. [10]Huang R, Cheng H, Guo H, et al., 2018. Hierarchical learning control with physical human-exoskeleton interaction. Inform Sci, 432:584-595. [11]Huang R, Cheng H, Qiu J, et al., 2019. Learning physical human-robot interaction with coupled cooperative primitives for a lower exoskeleton. IEEE Trans Autom Sci Eng, 16(4):1566-1574. [12]Ijspeert AJ, Nakanishi J, Schaal S, 2002. Movement imitation with nonlinear dynamical systems in humanoid robots. Proc IEEE Int Conf on Robotics and Automation, p.1398-1403. [13]Ijspeert AJ, Nakanishi J, Hoffmann H, et al., 2013. Dynamical movement primitives: learning attractor models for motor behaviors. Neur Comput, 25(2):328-373. [14]Jenison RL, Fissell K, 1995. A comparison of the von Mises and Gaussian basis functions for approximating spherical acoustic scatter. IEEE Trans Neur Netw, 6(5):1284-1287. [15]Kagawa T, Ishikawa H, Kato T, et al., 2015. Optimization-based motion planning in joint space for walking assistance with wearable robot. IEEE Trans Rob, 31(2):415-424. [16]Kazemi J, Ozgoli S, 2019. Real-time walking pattern generation for a lower limb exoskeleton, implemented on the exoped robot. Rob Auton Syst, 116:1-23. [17]Kazerooni H, Steger R, 2006. The Berkeley lower extremity exoskeleton. J Dynam Syst Meas Contr, 128(1):14-25. [18]Komura T, Nagano A, Leung H, et al., 2005. Simulating pathological gait using the enhanced linear inverted pendulum model. IEEE Trans Biomed Eng, 52(9):1502-1513. [19]Krüger V, Kragic D, Ude A, et al., 2007. The meaning of action: a review on action recognition and mapping. Adv Rob, 21(13):1473-1501. [20]Kulić D, Ott C, Lee D, et al., 2012. Incremental learning of full body motion primitives and their sequencing through human motion observation. Int J Rob Res, 31(3):330-345. [21]Lee SW, Yi T, Jung JW, et al., 2015. Design of a gait phase recognition system that can cope with EMG electrode location variation. IEEE Trans Autom Sci Eng, 14(3):1429-1439. [22]Paraschos A, Daniel C, Peters J, et al., 2013. Probabilistic movement primitives. Proc 26th Int Conf on Neural Information Processing Systems, p.2616-2624. [23]Paraschos A, Daniel C, Peters J, et al., 2018. Using probabilistic movement primitives in robotics. Auton Rob, 42(3):529-551. [24]Sankai Y, 2010. HAL: hybrid assistive limb based on cybernics. In: Kaneko M, Nakamura Y (Eds.), Robotics Research. Springer, Berlin, Heidelberg, p.25-34. [25]Sanz-Merodio D, Cestari M, Arevalo JC, et al., 2014. Generation and control of adaptive gaits in lower-limb exoskeletons for motion assistance. Adv Rob, 28(5):329-338. [26]Schaal S, Ijspeert A, Billard A, 2003. Computational approaches to motor learning by imitation. Phil Trans R Soc B Biol Sci, 358(1431):537-547. [27]Schaal S, Peters J, Nakanishi J, et al., 2005. Learning movement primitives. 11th Int Symp on Robotics Research, p.561-572. [28]Schmidhuber J, 2015. Deep learning in neural networks: an overview. Neur Netw, 61:85-117. [29]Spiegelhalter DJ, Best NG, Carlin BP, et al., 2002. Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol), 64(4):583-639. [30]Strausser KA, Kazerooni H, 2011. The development and testing of a human machine interface for a mobile medical exoskeleton. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4911-4916. [31]Stulp F, Sigaud O, 2012. Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning. https://hal.archives-ouvertes.fr/hal-00738463 [Accessed on Jan. 12, 2021] [32]Theodorou E, Buchli J, Schaal S, 2010. A generalized path integral control approach to reinforcement learning. J Mach Learn Res, 11:3137-3181. [33]Todorov E, Jordan MI, 2002. Optimal feedback control as a theory of motor coordination. Nat Neurosci, 5(11):1226-1235. [34]Tucker MR, Olivier J, Pagel A, et al., 2015. Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil, 12(1):1. [35]Veneman JF, Kruidhof R, Hekman EEG, et al., 2007. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neur Syst Rehabil Eng, 15(3):379-386. [36]Vukobratović M, Borovac B, 2004. Zero-moment point—thirty five years of its life. Int J Human Rob, 1(1):157-173. [37]Walsh CJ, Endo K, Herr H, 2007. A quasi-passive leg exoskeleton for load-carrying augmentation. Int J Human Rob, 4(3):487-506. [38]Xu B, Sun FC, 2018. Composite intelligent learning control of strict-feedback systems with disturbance. IEEE Trans Cybern, 48(2):730-741. [39]Yan TF, Cempini M, Oddo CM, et al., 2015. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Rob Auton Syst, 64:120-136. [40]Yang CG, Chen CZ, Wang N, et al., 2019. Biologically inspired motion modeling and neural control for robot learning from demonstrations. IEEE Trans Cogn Dev Syst, 11(2):281-291. [41]Yuan YX, Li ZJ, Zhao T, et al., 2020. DMP-based motion generation for a walking exoskeleton robot using reinforcement learning. IEEE Trans Ind Electron, 67(5):3830-3839. [42]Zoss AB, Kazerooni H, Chu A, 2006. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mechatr, 11(2):128-138. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>