[1]于建均,门玉森,阮晓钢,等.基于Kinect的Nao机器人动作模仿系统的研究与实现[J].智能系统学报编辑部,2016,11(2):180-187.[doi:10.11992/tis.201511020]
YU Jianjun,MEN Yusen,RUAN Xiaogang,et al.The research and implementation of behavior imitation system about Nao robot based on Kinect[J].CAAI Transactions on Intelligent Systems,2016,11(2):180-187.[doi:10.11992/tis.201511020]
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YU Jianjun,MEN Yusen,RUAN Xiaogang,et al.The research and implementation of behavior imitation system about Nao robot based on Kinect[J].CAAI Transactions on Intelligent Systems,2016,11(2):180-187.[doi:10.11992/tis.201511020]
基于Kinect的Nao机器人动作模仿系统的研究与实现
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第2期
页码:
180-187
栏目:
学术论文—智能系统
出版日期:
2016-04-25
- Title:
- The research and implementation of behavior imitation system about Nao robot based on Kinect
- Keywords:
- imitation learning; robot control; somatosensory perception; probability model; GMM; GMR
- 分类号:
- TP242.6
- 摘要:
- 为避开复杂繁琐的底层运动控制,使机器人能够通过学习实现运动技能的获取,有效提高其智能性,将体态感知技术与仿人机器人Nao相结合,以机器人的模仿学习框架为指导,开发并实现了基于Kinect的Nao机器人动作模仿系统。利用Kinect体感摄像机的骨骼跟踪技术,采集示教者骨骼点信息,经预处理后得到示教数据,通过高斯混合模型(GMM)对示教数据进行表征学习,经高斯混合回归(GMR)泛化处理后,映射到Nao机器人中,实现动作的模仿。实验结果表明,Nao机器人能够进行实时和离线的动作模仿,运动轨迹平滑而稳定,动作模仿的效果较好。
- Abstract:
- To avoid the complexity of the underlying motor control, make the robot realize motor skills through learning and improve its intelligence, Combining Somatosensory perception with humanoid robot Nao,this paper focuses on the research of robot imitation learning,constructs the behavior imitation system and implements Nao robot’s motion imitation using kinect based on the framework of imitation learning.By means of the skeleton tracking technology of motion-sensing camera,the bone point information is collected,then be pre-processed into demonstration data.The demonstration data is encoded for representative learning through Gaussian mixture model (GMM),and then the output generalized by Gaussian mixture regression (GMR) is mapped to Nao robot to realize the imitation of action.The experiment results indicate that Nao robot is able to implement behavior imitation in real-time and offline mode and gives good effect owing to the fact that the motion trajectory is smooth and stable.
- 参考文献/References:
-
[1] SCHAAL S. Is imitation learning the route to humanoid robots?[J]. Trends in cognitive sciences, 1999, 3(6): 233-242.
[2] BILLARD A, CALINON S, DILLMANN R, et al. Robot programming by demonstration[M]//SICILIANO B, KHATIB O. Springer Handbook of Robotics. Berlin Heidelberg: Springer, 2008: 1371-1394.
[3] ENGLERT P, PARASCHOS A, DEISENROTH M P, et al. Probabilistic model-based imitation learning[J]. Adaptive behavior, 2013, 21(5): 388-403.
[4] GRIBOVSKAYA E, KHANSARI-ZADEH S M, BILLARD A. Learning non-linear multivariate dynamics of motion in robotic manipulators[J]. The international journal of robotics research, 2011, 30(1): 80-117.
[5] LEE Sang H L, SUH I H, CALINON S, et al. Autonomous framework for segmenting robot trajectories of manipulation task[J]. Autonomous robots, 2015, 38(2): 107-141.
[6] ASFOUR T, AZAD P, GYARFAS F, et al. Imitation learning of dual-arm manipulation tasks in humanoid robots[J]. International journal of humanoid robotics, 2008, 5(2): 183-202.
[7] 于建均, 韩春晓, 阮晓钢, 等. 基于高斯过程的机器人模仿学习研究与实现[J]. 北京工业大学学报, 2015, 41(7): 1000-1004.YU Jianjun, HAN Chunxiao, RUAN Xiaogang, et al. Robot imitation learning based on Gaussian processes[J]. Journal of Beijing university of technology, 2015, 41(7): 1000-1004.
[8] CALINON S. Robot programming by demonstration: a probabilistic approach[M]. Lausanne: EPFL Press, 2009: 75-100.
[9] 赵姝颖, 徐文杰, 郑雪林, 等. 基于体感的机器人展示系统研究与开发[J]. 机器人技术与应用, 2012(6): 54-57. ZHAO Shuying, XU Wenjie, ZHENG Xuelin, et al. research and development of display system of Nao humanoid robot based on posture perception[J]. Robot technique and application of robot, 2012 (6): 54-57.
[10] ALMETWALLY I, MALLEM M. Real-time tele-operation and tele-walking of humanoid Robot Nao using Kinect Depth Camera[C]//Proceedings of the 10th IEEE International Conference on Networking, Sensing and Control (ICNSC). 2013 10th IEEE International Conference on Evry, France: IEEE, 2013: 463-466.
[11] KOENEMANN J, BENNEWITZ M. Whole-body imitation of human motions with a Nao humanoid[C]//Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).2012 7th ACM/IEEE International Conference onBoston, MA, USA: IEEE, 2012: 425-425.
[12] YANG Ningjia, DUAN Feng, WEI Yudi, et al. A study of the human-robot synchronous control system based on skeletal tracking technology[C]//Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, China, 2013: 2191-2196.
[13] BISHOP C M. Pattern recognition and machine learning[M]. New York: Springer, 2006: 423-455.
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备注/Memo
收稿日期:2015-11-23;改回日期:。
基金项目:国家自然科学基金项目(61375086);高等学校博士学科点专项科研基金项目(20101103110007).
作者简介:于建均,女,1965年生,副教授。主要研究方向为智能机器人的仿生自主控制、智能计算与智能优化控制、复杂过程建模、优化与控制。主持或参与国家"863"计划项目、国家自然科学基金等省部级科研项目以及横向科研课题多项;发表SCI、EI、ISTP收录论文40余篇,获国家发明专利、实用新型专利、国家软件著作权等10余项;门玉森,男,1991年生,硕士研究生,主要研究方向为机器学习、机器人技术。参与国家自然基金项目,发表学术论文3篇,获发明专利1项;阮晓钢,男,1960年生,教授,博士生导师,主要研究方向为人工智能与认知科学、机器人学与机器人技术、控制科学与工程等。主持科研课题20余项,发表学术论文400余篇,其中,被SCI和EI和ISTP检索200余次,获得多项国家发明专利、实用新型专利等。
通讯作者:门玉森.E-mail:menyusen0927@163.com.
更新日期/Last Update:
1900-01-01