CFP last date
20 May 2024
Reseach Article

CPG Design in Bipedal Locomotion by Machine Learning Techniques: A Review

by Rajeev Kumar, Laxman Singh, Rajdev Tiwari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 50
Year of Publication: 2019
Authors: Rajeev Kumar, Laxman Singh, Rajdev Tiwari
10.5120/ijca2019918682

Rajeev Kumar, Laxman Singh, Rajdev Tiwari . CPG Design in Bipedal Locomotion by Machine Learning Techniques: A Review. International Journal of Computer Applications. 182, 50 ( Apr 2019), 1-8. DOI=10.5120/ijca2019918682

@article{ 10.5120/ijca2019918682,
author = { Rajeev Kumar, Laxman Singh, Rajdev Tiwari },
title = { CPG Design in Bipedal Locomotion by Machine Learning Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2019 },
volume = { 182 },
number = { 50 },
month = { Apr },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number50/30535-2019918682/ },
doi = { 10.5120/ijca2019918682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:51.329483+05:30
%A Rajeev Kumar
%A Laxman Singh
%A Rajdev Tiwari
%T CPG Design in Bipedal Locomotion by Machine Learning Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 50
%P 1-8
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The natural species found in this world exhibit some form of intelligence in their conducts. The human being is the ultimate benchmark among all the species, which shows the intelligence in almost every conduct in their life. So in the world of artificial intelligence we are trying to mimic the behavior of the human being through the machines. The artificial counterpart of the human being who has the resemblance with the human being is called biped or humanoid. In this paper we review the machine learning techniques which are popular among researchers from last 10 to 15 years for learning task in robotics. Machine learning techniques specifically includes supervised learning, unsupervised learning and Reinforcement learning.

References
  1. Taga, G. A model of the neuro-musculo-skeletal system for human locomotion. i. emergence of basic gait. Biological Cybernetics, Vol. 73, pp. 97–111, 1995.
  2. Auke Jan Ijspeert. Central pattern generators for locomotion control in animals and robots: A review. ELSEVIER Journal on Neural Networks, Vol. 21, pp. 642-653, 2008.
  3. Taga, G. A model of the neuro-musculo-skeletal system for human locomotion. & real-time adaptability under various constraints. Biological Cybernetics, Vol. 73, pp. 113–121, 1995.
  4. Matsuoka, K. Mechanisms of frequency and pattern control in the neural rhythm generators. Biol. Cybern, Vol. 56, pp. 345–353, 1987.
  5. Taga, G. A model of the neuron-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance. Biological Cybernetics, Vol. 78, pp. 9–17, 1998.
  6. Taga, G., Yamaguchi, Y., Shimizu, H. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol. Cybern, Vol. 65, pp. 147–159, 1991.
  7. Chen-Chien Hsu, Wei-Yen Wang, Tung-Yuan Lin, Yin-Tien Wang and Teng-Wei Huang, Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots, International journal of humanoid robotics, Vol. 14, Issue 02, 2017.
  8. Takeshi U, Toshiharu H, Katsuji U. Evolution strategies for bipedal locomotion learning using nonlinear oscillators. Osaka University, Japan, AUG 2010.
  9. K. Noda, H. Arie, Y. Suga, and T. Ogata. Multimodel integration learning of robot behavior using deep neural networks. Robot autonomous systems, Vol-62, no. 6, p.p. 721-736, 2014.
  10. Manoj S. and Andy Ruina, Computer Optimization of a minimal biped model discovers walking and running, Biorobotics and Locomotion Laboratory, Cornell University, Ithaca, NY USA, 2006 Nature.
  11. Guang Lei Liu, Maki K. Habib, Keigo Watanabe, and Kiyotaka Izumi, The Design of Central Pattern Generators based on the Matsuoka Oscillator to Generate Rhythmic Human Like Movement for Biped Robots, Saga University Honjomachi, Saga 840-8502, Japan, May 2007.
  12. Lingling Chen, Peng Yang, He Chen, Xi Guo, Gait optimization of Biped Robot Based on Mix Encoding Genetic Algorithm, School of Electrical Engineering and Automation , Hebei University of Technology, Tianjin, China, IEEE 2007.
  13. PanduRangaVundavilli, Dilip Kumar Pratihar, Soft computing based gait planners for a dynamically balanced biped robot negotiating sloping surfaces, IIT Kharagpur, India, 2008 ELSEVIER.
  14. Slawomir Grzonka, Andreas Karwath, Frederic Dijoux, and Wolfram Burgard, Activity-Based Estimation of Human Trajectories, IEEE Transactions on robotics , 2011
  15. J.Kim, Naveen Kumar,Vikas Panwar, J.H. Borm and J.Chai; Adaptive neural controller for visual servoing of Robot Manipulators with camera-in-hand configuration, Journal of mechanical science and Technology, Vol. 28, No. 8, pp 2313-2323, 2012
  16. Emmanuel senft, Paul Baxter, James Kennedy, Supervised autonomy for online learning in human robot interaction, Elsevier pattern recognition letters, March 31,2017.
  17. Kai hu, Dongheui Lee, Bipedal locomotion primitive learning, Control and prediction from human data, 10th IFAC symposium on robot control, September 5-7, 2012
  18. Sidahmed Benabderrahmane, Combining boosting machine learning and swarm intelligence for real
  19. time object detection and tracking: towards new meta heuristics boosting classifiers, Springer Nature, November 22, 2017
  20. S. Levine et al, “End to End training of deep visuometer policies ” Journal of Machine Learning, vol-17, no.35 Apr 2016.
  21. Kaiyang Yin, Muye Pang, Kui Xiang, Jing Chen, Shenpei Zhou, “Fuzzy iterative learning control starategy for powered ankle prosthesis” , Springer Nature Sigapore Pte. Ltd. March 2018.
  22. J.P. Ferreira, M.crisostomo, A.P. Coimbra, B.Ribeiro, Simulation control of a biped robot with support vector regression, Proceeding IEEE Int. Symp. Int. signal process, 2007, p.p. 1-7
  23. Shouyi Wang et al., Machine Learning Algorithms in Bipedal Robot Control, IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, VOL-42, NO.-5, SEPTEMBER 2012.
  24. Min-Su Kim and Jun Ho Oh, Posture control of a Humanoid Robot with a compliant ankle joint, International journal of humanoid robotics, Vol. 07, No. 01, p.p. 5-29, 2010.
  25. Hisashi Sugiura et al., Reactive self collision avoidance with dynamic task prioritization for humanoid robots, International journal of humanoid robotics, Vol. 07, No. 01, p.p. 31-54, 2010.
  26. Q.WU and J.CHEN, Effects of ramp angle and mass distributions on passive dynamic gait – An experimental study, International journal of humanoid robotics, Vol. 07, No. 01, p.p. 55-72, 2010.
  27. J.G.Daniel Kassen and Martijn Wisse, Fall detection of two legged walking robots using multi-way principal component analysis, International journal of humanoid robotics, Vol. 07, No. 01, p.p. 73-93, 2010
  28. Qirong Mao, Xioajia Wang, Yongzhao Zhan, Speech emotion recognition method based on improved decision tree and layered feature selection, International journal of humanoid robotics, Vol. 07, No. 02, p.p. 245-261, 2010
  29. Armin Hornung, Stefan Obwald, Daniel Maier and Maren Bennewitz, Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments, International journal of humanoid robotics, Vol. 11, Issue 02 (2014), 1441002
  30. Toshiya Nishi and Tomomichi Sugihara, Motion planning of a Humanoid Robot in a complex environment using RRT and Spatiotemporal Post-Processing Techniques, International journal of humanoid robotics, Vol. 11, Issue 02 (2014), 1441003
  31. Said G. Khan, Guido Herrmann, Mubarak Al Grafi, Tony Pipe and Chris Melhuish, Compliance control and Human Robot Interaction: Part 1 – Survey, International journal of humanoid robotics, Vol. 11, Issue 03 (2014), 1430001
  32. Said G. Khan, Guido Herrmann, Mubarak Al Grafi, Tony Pipe and Chris Melhuish, Compliance control and Human Robot Interaction: Part II – Experimental Examples, International journal of humanoid robotics, Vol. 11, Issue 03 (2014), 1430002
  33. Javier Moya, Javier Ruiz-del-Solar, Marcos Orchard and Isao Parra-Tsunekawa, Fall Detection and Damage Reduction in Biped Humanoid Robots, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155001
  34. Martin D. Cooney, Shuichi Nishio and Hiroshi Ishiguro, Importance of Touch for Conveying Affection in a multimodal Interaction with a small Humanoid Robot, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155002
  35. Yeoun-Jae Kim, Joon-Yong Lee and Ju-Jang Lee, A Torso moving Balance Control Strategy for a walking Biped Robot Subject to External Continuous Forces, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155003
  36. Jonathan Spitz, Eric Sidorov and Miriam Zacksenhouse, Humanoids can take advantages of Crab-Walking Gaits, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155004
  37. Wenzhen Yang, Xinli Wu and Hua Zhang, Workspace Modeling and Analysis for Dexterous Hands, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155006
  38. Jan Kedzierski, Pawel Kaczmarek, Michal Dziergwa and Krzysztof Tchon, Design for a Robotic Companion, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155007
  39. Francisco Martin, Carlos E. Aguero and Jose M. Canas, Active Visual Perception for Humanoid Robots, International journal of humanoid robotics, Vol. 12, Issue 01 (2015), 155009
  40. Sung Taek Cho and Seul Jung, Combining two Control Techniques for the Fast Movement of a Two-Wheel Mobile Robot, International journal of humanoid robotics, Vol. 12, Issue 02 (2015), 1550020
  41. Yeoun-Jae Kim, Joon-Yong Lee and Ju-Jang Lee, A Balance control strategy for a Walking Biped Robot under Unknown Lateral External Force using a Genetic Algorithm, International journal of humanoid robotics, Vol. 12, Issue 02 (2015), 1550021
  42. Rok Vuga, Bojan Nemec and Ales Ude, Enhanced policy Adaptation Through Directed Explorative Learning, International journal of humanoid robotics, Vol. 12, Issue 03 (2015), 1550028
  43. Jemin Hwangbo, Christian Gehring, Hannes Sommer, Roland Siegwart and Jonas Buchli, Policy learning with an Efficient Black-Box Optimization Algorithm, , International journal of humanoid robotics, Vol. 12, Issue 03 (2015), 1550029
  44. Akihiko Yamaguchi, Christopher G. Atkeson and Tsukasa Ogasawara, Pouring skills and Learning Modeled from Human Demonstrations, , International journal of humanoid robotics, Vol. 12, Issue 03 (2015), 1550030
  45. Peter Kaiser, Nikolaus VahrenKamp, Fabian Schultje, Julia Borras and Tamim Asfour, Extraction of Whole-Body Affordances for Loco-Manipulation Tasks, , International journal of humanoid robotics, Vol. 12, Issue 03 (2015), 1550031
  46. Olivier Hugues, Vincent Weistroffer, Alexis Paljic, Philippe Fuchs, Ahmad Abdul Karim, Thibaut Gaudin and Axel Buendia, Determining the important subjective criteria in the Perception of Human like Robot Movements Using Virtual Reality, International journal of humanoid robotics, Vol. 13, Issue 02 (2016), 1550033
  47. Roberta AIo and Giacomo Mantriota, Optimal Grip Points with Human Hand, International journal of humanoid robotics, Vol. 13, Issue 02 (2016), 1550036
  48. Juan Alejandro Castano Zhibin Li, Chengxu Zhou, Nikos Tsagarakis and Darwin Caldwell, Dynamic and Reative Walking for Humanoid Robots Based on Foot Placement Control, International journal of humanoid robotics, Vol. 13, Issue 02 (2016), 1550041
  49. Meteb M. Altaf, Bassant M. Elbagoury, Fahad Alraddady and Mohamed Roushdy, Extended Case-Based Behavior Control for Multi-Humanoid Robots, International journal of humanoid robotics, Vol. 13, Issue 02 (2016), 1550035
  50. Kai Xu, Huan Liu, Yuheng Du and Xiangyang Zhu, A Comparative study for Postural Synergy Synthesis Using Linear and Nonlinear Methods, International journal of humanoid robotics, Vol. 13, Issue 03 (2016), 1650009
  51. Takeshi Nishida, Yuki Okatani and Kenjiro Tadakuma, Development of Universal Robot Gripper Using MRα Fluid, , International journal of humanoid robotics, Vol. 13, Issue 04 (2016), 1650017
  52. Jumpei Arata, Kazuo Kiguchi, Masashi Hattori, Masamichi Sakaguchi, Ryu Nakadate, Susumu Oguri and Makoto Hashizume, A Microsurgical Robotic System that Induces a Multisensory Illusion, , International journal of humanoid robotics, Vol. 13, Issue 04 (2016), 1650018
  53. Sahab Omran, Sophie Sakka and Yannick Aoustin, Effects of COM Vertical Oscillation on Joint Torques During 3D Walking of Humanoid Robots, , International journal of humanoid robotics, Vol. 13, Issue 04 (2016), 1650019
  54. Daniele Cafolla and Macro Ceccarelli, Design and Simulation of a Cable-Driven Vertebra-Based Humanoid Torso, , International journal of humanoid robotics, Vol. 13, Issue 04 (2016), 1650015
  55. Jing Zhao and Yuan Wei, A Novel Algorithm of Human-Like Motion Planning for Robotic Arms, International journal of humanoid robotics, Vol. 14, Issue 01 (2017), 1650023
  56. Wenjun Ye, Zhijun Li, Chenguang Yang, Fei Chen and Chun-Yi Su, Motion Detection Enhanced Control of an Upper Limb Exoskeleton Robot for Rehabilitation Training, International journal of humanoid robotics, Vol. 14, Issue 01 (2017), 1650031
  57. Wenzhen Yang, Xinli Wu and Shiguang Yu, A Master-Slave Control Method for Dexterous Hands with Shaking Elimination Strategy, International journal of humanoid robotics, Vol. 14, Issue 01 (2017), 1650016
  58. Li Chen, Wang-Rim Choi, Jeong-Gu Lee, Yi-Gon Kim, Hong-Sik Moon and Young-Chul Bae, Oil-Tank Weld Detection Using EMAT, International journal of humanoid robotics, Vol. 14, Issue 02 (2017), 1750008
  59. Howon Lee and Jangmyung Lee, Optimal Control of an Inverted Ball-Driving Robot Based upon Slip Patterns, International journal of humanoid robotics, Vol. 15, Issue 02 (2018), 1850007
  60. Daniel Herrera, Flavio Roberti, Ricardo Carelli, Victor Andaluz, Jose Varela, Jessica Ortiz and Paul Canseco, Modeling and Path-Following Control of a Wheelchair in Human-Shared Environments, International journal of humanoid robotics, Vol. 15, Issue 02 (2018), 1850010
Index Terms

Computer Science
Information Sciences

Keywords

Biped Central pattern generator (CPG) Deep Learning Machine Learning neural oscillators.