A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion

We present a model of motor learning based on a combination of Operational Space Control and Optimal Control. Anticipatory processes are used both in the learning of the dynamics model of the system and in the coordination between both types of control. In order to illustrate the proposed model and associated control method, we apply these principles to the control of a simplified virtual humanoid performing a stand-up task starting from a crouching posture.

[1]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[2]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[3]  Richard J. Duro,et al.  Biologically inspired robot behavior engineering , 2003 .

[4]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[5]  Joachim Hoffmann,et al.  Exploiting redundancy for flexible behavior: unsupervised learning in a modular sensorimotor control architecture. , 2007, Psychological review.

[6]  Duncan Potts,et al.  Incremental learning of linear model trees , 2004, ICML.

[7]  Stefan Schaal,et al.  Local dimensionality reduction for locally weighted learning , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[8]  Y Uno,et al.  Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. , 1999, Journal of neurophysiology.

[9]  Bernhard Schölkopf,et al.  Learning Inverse Dynamics: a Comparison , 2008, ESANN.

[10]  Gregor Schöner,et al.  The uncontrolled manifold concept: identifying control variables for a functional task , 1999, Experimental Brain Research.

[11]  Hiroyuki Miyamoto,et al.  Computing the optimal trajectory of arm movement: the TOPS (Task Optimization in the Presence of Signal-Dependent Noise) model , 2003 .

[12]  Stefan Schaal,et al.  Locally Weighted Projection Regression : An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space , 2000 .

[13]  E. Todorov Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[14]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[15]  John Hallam,et al.  From Animals to Animats 10 , 2008 .

[16]  Oussama Khatib,et al.  A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..

[17]  Michel Tenenhaus La r?gression PLS: th?orie et pratique , 1998 .

[18]  P. R. Davidson,et al.  Widespread access to predictive models in the motor system: a short review , 2005, Journal of neural engineering.

[19]  Sethu Vijayakumar,et al.  Adaptive Optimal Control for Redundantly Actuated Arms , 2008, SAB.

[20]  Emmanuel Guigon,et al.  Optimality, stochasticity, and variability in motor behavior , 2008, Journal of Computational Neuroscience.

[21]  Florence Billet,et al.  The HuMAnS toolbox, a homogenous framework for motion capture, analysis and simulation , 2006 .

[22]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[23]  Mitsuo Kawato,et al.  MOSAIC Model for Sensorimotor Learning and Control , 2001, Neural Computation.

[24]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[25]  Stefan Schaal,et al.  LWPR: A Scalable Method for Incremental Online Learning in High Dimensions , 2005 .

[26]  Oussama Khatib,et al.  Control of Free-Floating Humanoid Robots Through Task Prioritization , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[27]  Stefan Schaal,et al.  Learning inverse kinematics , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[28]  Stefano Chiaverini,et al.  Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators , 1997, IEEE Trans. Robotics Autom..

[29]  Gene H. Golub,et al.  Matrix computations , 1983 .

[30]  J. Foley The co-ordination and regulation of movements , 1968 .

[31]  E. Todorov,et al.  A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[32]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[33]  P. Fitts The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[34]  M. Kawato Optimization and learning in neural networks for formation and control of coordinated movement , 1993 .

[35]  P. Bidaud,et al.  Stability Measure of Postural Dynamic Equilibrium Based on Residual Radius , 2008 .

[36]  M. Desmurget,et al.  Computational motor control: feedback and accuracy , 2008, The European journal of neuroscience.

[37]  Michael I. Jordan,et al.  A Minimal Intervention Principle for Coordinated Movement , 2002, NIPS.

[38]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[39]  Brian Scassellati,et al.  A Fast and Efficient Model for Learning to Reach , 2005, Int. J. Humanoid Robotics.

[40]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[41]  Emmanuel Guigon,et al.  Computational Motor Control : Redundancy and Invariance , 2007 .

[42]  Mitsuo Kawato,et al.  Multiple Model-Based Reinforcement Learning , 2002, Neural Computation.

[43]  David E. Meyer,et al.  Attention and performance XIV (silver jubilee volume): synergies in experimental psychology, artificial intelligence, and cognitive neuroscience , 1993 .