On the Kinematic Motion Primitives (kMPs) – Theory and Application

Human neuromotor capabilities guarantee a wide variety of motions. A full understanding of human motion can be beneficial for rehabilitation or performance enhancement purposes, or for its reproduction on artificial systems like robots. This work aims at describing the complexity of human motion in a reduced dimensionality, by means of kinematic Motion Primitives (kMPs). A set of five invariant kMPs are identified for periodic motions, and a set of two kMPs for discrete motions. It is shown how these two sets of kMPs can be combined to synthesize more complex motion as the simultaneous execution of the periodic and the discrete motions. The results reported are an evidence of the theory of Central Pattern Generators (CPG), showing its effects on the kinematics, and are related to what presented in the literature on the Motor Primitives extracted from EMG signals. Experimental tests with the COmpliant huMANoid (COMAN) were performed to show that the kMPs extracted from human subjects can be used to transfer the features of human locomotion to the gait of a robot.

[1]  F. Lacquaniti,et al.  Five basic muscle activation patterns account for muscle activity during human locomotion , 2004, The Journal of physiology.

[2]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[3]  Auke Jan Ijspeert,et al.  Central pattern generators for locomotion control in animals and robots: A review , 2008, Neural Networks.

[4]  T. Brown.  The intrinsic factors in the act of progression in the mammal , 1911 .

[5]  Sten Grillner,et al.  Biological Pattern Generation: The Cellular and Computational Logic of Networks in Motion , 2006, Neuron.

[6]  Ludovic Righetti,et al.  Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives , 2011, Auton. Robots.

[7]  Nikolaos G. Tsagarakis,et al.  The design of the lower body of the compliant humanoid robot “cCub” , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  S. Grillner.  Neurobiological bases of rhythmic motor acts in vertebrates. , 1985, Science.

[9]  Dagmar Sternad,et al.  Towards a Unified Theory of Rhythmic and Discrete Movements — Behavioral, Modeling and Imaging Results , 2008 .

[10]  Francesco Lacquaniti,et al.  Patterned control of human locomotion , 2012, The Journal of physiology.

[11]  Auke Ijspeert,et al.  Modeling discrete and rhythmic movements through motor primitives: a review , 2010, Biological Cybernetics.

[12]  N. Hogan,et al.  On rhythmic and discrete movements: reflections, definitions and implications for motor control , 2007, Experimental Brain Research.

[13]  E Bizzi,et al.  Motor learning through the combination of primitives. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[14]  M. Dimitrijevic,et al.  Evidence for a Spinal Central Pattern Generator in Humans a , 1998, Annals of the New York Academy of Sciences.

[15]  Philippe Lefèvre,et al.  A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination , 2009, Neural Computation.

[16]  J. F. Soechting,et al.  Invariant characteristics of a pointing movement in man , 1981, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  E. Marder,et al.  Central pattern generators and the control of rhythmic movements , 2001, Current Biology.

[18]  D. Sternad,et al.  Interaction between discrete and rhythmic movements: reaction time and phase of discrete movement initiation during oscillatory movements , 2003, Brain Research.

[19]  F. Delcomyn.  Neural basis of rhythmic behavior in animals. , 1980, Science.

[20]  F. Lacquaniti,et al.  Coordination of Locomotion with Voluntary Movements in Humans , 2005, The Journal of Neuroscience.

[21]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[22]  Nikolaos G. Tsagarakis,et al.  Efficient human-like walking for the compliant huMANoid COMAN based on linematic Motion Primitives (kMPs) , 2012, 2012 IEEE International Conference on Robotics and Automation.

[23]  G. Metta,et al.  A modular bio-inspired architecture for movement generation for the infant-like robot iCub , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[24]  O. Kiehn,et al.  Physiological, anatomical and genetic identification of CPG neurons in the developing mammalian spinal cord , 2003, Progress in Neurobiology.

[25]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[26]  S. Schaal,et al.  Rhythmic arm movement is not discrete , 2004, Nature Neuroscience.

[27]  Stefan Schaal,et al.  Interaction of rhythmic and discrete pattern generators in single-joint movements , 2000 .

[28]  T. Brown,et al.  The Factors in Rhythmic Activity of the Nervous System , 1912 .

[29]  R. Bongers,et al.  The dependence of discrete movements on rhythmic movements: Simple RT during oscillatory tracking , 1994 .

[30]  E. Bizzi,et al.  The construction of movement by the spinal cord , 1999, Nature Neuroscience.

[31]  P. Beek,et al.  Discrete and cyclical movements: unified dynamics or separate control? , 2004, Acta psychologica.

[32]  Nikolaos G. Tsagarakis,et al.  A human-like walking for the COmpliant huMANoid COMAN based on CoM trajectory reconstruction from kinematic Motion Primitives , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[33]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[34]  E. Bizzi,et al.  Article history: , 2005 .