An Approach to Robot Hand-eye Coordination Inspired by Human Infant Development
ZHANG Xin, ZHOU Changle, JIANG Min, CHAO Fei
Fujian Provincial Key Laboratory of Brain-like Intelligent Systems, Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China
The objective of this research is to implement an autonomous learning approach to robotic hand-eye coordination ability, so as to bring higher adaptive ability to robots in the practical environment. The approach is inspired by human infant's developmental procedure, a brain-like computational structure is constructed to simulate human brain cortices of controlling hand-eye coordination; and then, a behavioral pattern is adopted from infant development when forming hand-eye coordination. The combination of the computational structure and the behavioral pattern is applied to building a novel robotic hand-eye coordination learning algorithm. This work is supported by experimental evaluation, which shows that this approach is able to drive the robot to learn hand-eye coordination successfully; the robot also shows staged behavior change, which is similar to the features of human infant development; in addition, the robot exhibits fast learning speed.
Kumar P P, Behera L. Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map[J]. Robotics and Autonomous Systems, 2010, 58(8): 978-990.
[7]
Nori F, Natale L, Sandini G, et al. Autonomous learning of 3d reaching in a humanoid robot[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2007: 1142-1147.
[10]
Meng Q, Lee M H, Hinde C J. Robot competence development by constructive learning[J]. Lecture Notes in Electrical Engineering, 2010, 48: 15-26.
[6]
马如奇, 董为, 杜志江, 等.主被动混合式微创手术机械臂机构设计及灵巧度优化[J].机器人, 2013, 35(1): 81-89. Ma R Q, Dong W, Du Z J, et al. Mechanical design and dexterity optimization for hybrid active-passive minimally invasive surgical manipulator[J]. Robot, 2013, 35(1): 81-89.
[19]
Mathew A, Cook M. The control of reaching movements by young infants[J]. Child Development, 1990, 61: 1238-1257.
[14]
Chao F, Hu L, Shi M, et al. Robotic 3D reaching through a development-driven double neural network architecture[M]//Advances in Intelligent and Soft Computing, vol.123. Berlin, Germany: Springer-Verlag, 2012: 179-184.
[20]
Cassanello C R, Nihalani A T, Ferrera V P. Neuronal responses to moving targets in monkey frontal eye fields[J]. Journal of Neurophysiology, 2008, 100(3): 1544-1556.
[17]
Khamassi M, Lallee S, Enel P, et al. Robot cognitive control with a neurophysiologically inspired reinforcement learning model[J]. Frontiers in Neurorobotics, 2011, 5(1): 1-14.
[22]
Cohen Y E, Andersen R A. A common reference frame for movement plans in the posterior parietal cortex[J]. Nature Reviews Neuroscience, 2002, 3(7): 553-562.
[3]
Asada M, Hosoda K, Kuniyoshi Y, et al. Cognitive developmental robotics: A survey[J]. IEEE Transactions on Autonomous Mental Development, 2009, 1(1): 12-34.
[16]
Chao F, Lin H, Jiang M, et al. Integration of brain-like computational structure and infant behaviorial pattern for robotic hand-eye coordination[C]//Proceedings of the 12th International Conference on Control, Automation, Robotics and Vision. 2012: 100-105.
[21]
Chao F, Lee M H, Lee J J. A developmental algorithm for ocularmotor coordination[J]. Robotics and Autonomous Systems, 2010, 58(3): 239-248.
[1]
Weng J. Symbolic models and emergent models: A review[J]. IEEE Transactions on Autonomous Mental Development, 2012, 4(1): 29-53.
[4]
Stoytchev A. Some basic principles of developmental robotics[J]. IEEE Transactions on Autonomous Mental Development, 2009, 1(2): 122-130.
[9]
Meng Q, Lee M H. Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks[J]. Connection Science, 2007, 19(1): 25-52.
[12]
Huelse M, McBride S, Lee M. Developmental robotics architecture for active vision and reaching[C]//IEEE International Conference on Development and Learning. Piscataway, USA: IEEE, 2011: 1-6.
[15]
Chao F, Lin H, Jiang M, et al. Robotic a developmental constraint driven approach to developmental robotic hand-eye coordination[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, USA: IEEE, 2012: 179-184.
[13]
Chinellato E, Antonelli M, Grzyb B J, et al. Implicit sensorimotor mapping of the peripersonal space by gazing and reaching[J]. IEEE Transactions on Autonomous Mental Development, 2011, 3(1): 43-53.
[18]
Chao F, Zhang X, Lin H X, et al. Learning robotic hand-eye coordination through a developmental constraint driven approach[J]. International Journal of Automation and Computing, 2013, 10(5): 414-424.
[23]
Piek J, Carman R. Developmental profiles of spontaneous movements in infants[J]. Early Human Development, 1994, 39(2): 109-126.
[2]
于化龙, 朱长明, 刘海波, 等.发育机器人研究综述[J].智能系统学报, 2007, 2(4):34-39. Yu H L, Zhu C M, Liu H B, et al. A survey on developmental robotics[J]. CAAI Transactions on Intelligent Systems, 2007, 2(4): 34-39.
[11]
Huelse M, McBride S, Law J, et al. Integration of active vision and reaching from a developmental robotics perspective[J]. IEEE Transactions on Autonomous Mental Development, 2010, 2(4): 355-367.
[24]
Butko N J, Fasel I R, Movellan J R. Learning about humans during the first 6 minutes of life[C]//IEEE International Conference on Development and Learning. Piscataway, USA: IEEE, 2006.
[25]
Lee M H, Meng Q, Chao F. Staged competence learning in developmental robotics[J]. Adaptive Behavior, 2007, 15(3): 241-255.
[5]
于凌涛, 王正雨, 于鹏, 等.基于动态视觉引导的外科手术机器人器械臂运动方法[J].机器人, 2013, 35(2): 162-170. Yu L T, Wang Z Y, Yu P, et al. A kinematics method based on dynamic visual navigation for the instrument arm of surgical robot[J]. Robot, 2013, 35(2): 162-170.
