[1] Yong Y S, Huang Yanjiang, Chiba R, et al. Teaching-playback robot manipulator system in consideration of singularities[C]// Proceedings of the 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 2013:453-458.
[2] Lee S H, Li Chengjie, Kim D H, et al. The direct teaching and playback method for robotic deburring system using the adaptiveforce-control[C]// Proceedings of the 2009 IEEE International Symposium on Assembly and Manufacturing. 2009:235-241.
[3] Choi S, Eakins W, Rossano G, et al. Lead-through robot teaching[C]// Proceedings of the 2013 IEEE Conference on Technologies for Practical Robot Applications. 2013,doi: 10.1109/TePRA.2013.6556347.
[4] Schaal S. Is imitation learning the route to humanoid robots?[J]. Trends in Cognitive Sciences, 1999,3(6):233-242.
[5] Calinon S. Robot Programming by Demonstration: A Probabilistic Approach[M]. EPFL Press, 2009.
[6] 张利格,毕树生,高金磊. 仿人机器人复杂动作设计中人体运动数据提取及分析方法[J]. 自动化学报, 2010,36(1):107-112.
[7] 李少波,赵毅夫,赵群飞,等. 机器人的人体姿态动作识别与模仿算法[J]. 计算机工程, 2013,39(8):181-186.
[8] Erol A, Bebis G, Nicolescu M, et al. Vision-based hand pose estimation: A review[J]. Computer Vision and Image Understanding, 2007,108(1-2):52-73.
[9] Mitra S, Acharya T. Gesture recognition: A survey[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2007,37(3):311-324.
[10]Dewaele G, Devernay F, Horaud R. Hand motion from 3D point trajectories and a smooth surface model[C]// Proceedings of the 2004 European Conference on Computer Vision. 2004:495-507.
[11]Ren Zhou, Yuan Junsong, Meng Jingjing, et al. Robust part-based hand gesture recognition using Kinect sensor[J]. IEEE Transactions on Multimedia, 2013,15(5):1110-1120.
[12]Ibaez R, Soria , Teyseyre A, et al. Easy gesture recognition for Kinect[J]. Advances in Engineering Software, 2014,76:171-180.
[13]Xiao Yang, Zhang Zhijun, Beck A, et al. Human: Robot interaction by understanding upper body gestures[J]. Presence: Teleoperators and Virtual Environments, 2014,23(2):133-154.
[14]Du Guanglong, Zhang Ping. Human-manipulator interface using hybrid sensors with Kalman filters and adaptive multi-space transformation[J]. Measurement, 2014,55:413-422.
[15]Bueno M, Díaz-Vilario L, Martínez-Sánchez J, et al. Metrological evaluation of KinectFusion and its comparison with Microsoft Kinect sensor[J]. Measurement, 2015,73:137-145.
[16]朱特浩,赵群飞,夏泽洋. 利用Kinect的人体动作视觉感知算法[J]. 机器人, 2014,36(6):647-653.
[17]Yavan E, Uar A. Gesture imitation and recognition using Kinect sensor and extreme learning machines[J]. Measurement, 2016,94:852-861.
[18]Wei S E, Ramakrishna V, Kanade T, et al. Convolutional pose machines[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016:4724-4732.
[19]Cao Zhe, Simon T, Wei S E, et al. Realtime multi-person 2D pose estimation using part affinity fields[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017:1302-1310.
[20]Simon T, Joo H, Matthews I, et al. Hand keypoint detection in single images using multiview bootstrapping[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017:4645-4653.
[21]Pokorny F T, Kragic D. Classical grasp quality evaluation: New algorithms and theory[C]// Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2013:3493-3500.
[22]Bohg J, Morales A, Asfour T, et al. Data-driven grasp synthesis: A survey[J]. IEEE Transactions on Robotics, 2014,30(2):289-309.
[23]Stouraitis T, Hillenbrand U, Roa M A. Functional power grasps transferred through warping and replanning[C]// Proceedings of the 2015 IEEE International Conference on Robotics and Automation. 2015:4933-4940.
[24]Pokorny F T, Hang Kaiyu, Kragic D. Grasp moduli spaces[C]// Proceedings of the 2013 IEEE International Conference on Robotics and Automation. 2013:389-396.
[25]Goldfeder C, Allen P K. Data-driven grasping[J]. Autonomous Robots, 2011,31(1):1-20.
[26]Goldfeder C, Ciocarlie M, Dang Hao, et al. The Columbia grasp database[C]// Proceedings of the 2009 IEEE International Conference on Robotics and Automation. 2009:1710-1716.
[27]Brook P, Ciocarlie M, Hsiao K. Collaborative grasp planning with multiple object representations[C]// Proceedings of the 2011 IEEE International Conference on Robotics and Automation. 2011:2851-2858.
[28]Kehoe B, Matsukawa A, Candido S, et al. Cloud-based robot grasping with the Google object recognition engine[C]// Proceedings of the 2013 IEEE International Conference on Robotics and Automation. 2013:4263-4270.
[29]Detry R, Ek C H, Madry M, et al. Learning a dictionary of prototypical grasp-predicting parts from grasping experience[C]// Proceedings of the 2013 IEEE International Conference on Robotics and Automation. 2013:601-608.
[30]Herzog A, Pastor P, Kalakrishnan M, et al. Learning of grasp selection based on shape-templates[J]. Autonomous Robots, 2014,36(1-2):51-65.
[31]Zhang Li, Ciocarlie M, Hsiao K. Grasp evaluation with graspable feature matching[C]// Proceedings of the 2011 RSS Workshop on Mobile Manipulation: Learning to Manipulate. 2011.
[32]Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps[J]. International Journal of Robotics Research, 2015,34(4-5):705-724.
[33]Kappler D, Bohg J, Schaal S. Leveraging big data for grasp planning[C]// Proceedings of the 2015 IEEE International Conference on Robotics and Automation. 2015:4304-4311.
[34]Laskey M, Mahler J, McCarthy Z, et al. Multi-armed bandit models for 2D grasp planning with uncertainty[C]// Proceedings of the 2015 IEEE Conference on Automation Science and Engineering. 2015:572-579.
[35]Mahler J, Pokorny F T, Hou B, et al. Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards[C]// Proceedings of the 2016 IEEE International Conference on Robotics and Automation. 2016:1957-1964.
[36]Mahler J, Liang J, Niyaz S, et al. Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics[DB/OL]. https://arxiv.org/pdf/1703.09312v3.pdf, 2017-08-08.
[37]Mahler J, Matl M, Liu Xinyu, et al. Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning[DB/OL]. https://arxiv.org/pdf/1709.06670v1.pdf,2017-09-19.
[38]Cutler M, How J P. Efficient reinforcement learning for robots using informative simulated priors[C]// Proceedings of the 2015 IEEE International Conference on Robotics and Automation. 2015:2605-2612.
[39]Koos S, Mouret J B, Doncieux S. Crossing the reality gap in evolutionary robotics by promoting transferable controllers[C]// Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. 2010:119-126.
[40]Kober J, Bagnell J A, Peters J. Reinforcement learning in robotics: A survey[J]. International Journal of Robotics Research, 2013,32(11):1238-1274.
[41]Cutler M, Walsh T J, How J P. Reinforcement learning with multi-fidelity simulators[C]// Proceedings of the 2014 IEEE International Conference on Robotics and Automation. 2014:3888-3895.
[42]Cully A, Clune J, Tarapore D, et al. Robots that can adapt like animals[J]. Nature, 2015,521(7553):503-507.
[43]Rajeswaran A, Ghotra S,Ravindran B, et al. EPOpt: Learning robust neural network policies using model ensembles[C]// Proceedings of the 2017 International Conference on Learning Representations. 2017.
[44]Christiano P, Shah Z, Mordatch I, et al. Transfer from Simulation to Real World Through Learning Deep Inverse Dynamics Model[DB/OL]. https://arxiv.org/pdf/1610.03518v1.pdf, 2016-10-11.
[45]Hanna J P, Stone P. Grounded action transformation for robot learning in simulation[C]// Proceedings of the 31st AAAI Conference on Articial Intelligence. 2017:3834-3840.
[46]于建均,徐骢驰,阮晓钢,等. 基于神经网络的机械臂的模仿学习研究[J]. 控制工程, 2017(11):2368-2373.
[47]Schulman J, Moritz P, Levine S, et al. High-dimensional Continuous Control Using Generalized Advantage Estimation[DB/OL]. https://arxiv.org/pdf/1506.02438v5.pdf, 2016-09-09.
[48]Rusu A A, Rabinowitz N C, Desjardins G, et al. Progressive Neural Networks[DB/OL]. https://arxiv.org/pdf/1606.04671v3.pdf, 2016-09-07.
[49]Rusu A A, Vecerik M, Rothorl T, et al. Sim-to-real Robot Learning from Pixels with Progressive Nets[DB/OL].https://arxiv.org/pdf/1610.04286v1.pdf, 2016-10-13.
[50]Rahmatizadeh R, Abolghasemi P, Behal A, et al. From Virtual Demonstration to Real-world Manipulation Using LSTM and MDN[DB/OL].https://arxiv.org/pdf/1603.03833v4.pdf, 2017-11-22. |