Robot-assisted adaptive training: custom force fields for teaching movement patterns

Based on recent studies of neuro-adaptive control, we tested a new iterative algorithm to generate custom training forces to "trick" subjects into altering their target-directed reaching movements to a prechosen movement as an after-effect of adaptation. The prechosen movement goal, a sinusoidal-shaped path from start to end point, was never explicitly conveyed to the subject. We hypothesized that the adaptation would cause an alteration in the feedforward command that would result in the prechosen movement. Our results showed that when forces were suddenly removed after a training period of 330 movements, trajectories were significantly shifted toward the prechosen movement. However, de-adaptation occurred (i.e., the after-effect "washed out") in the 50-75 movements that followed the removal of the training forces. A second experiment suppressed vision of hand location and found a detectable reduction in the washout of after-effects, suggesting that visual feedback of error critically influences learning. A final experiment demonstrated that after-effects were also present in the neighborhood of training-44% of original directional shift was seen in adjacent, unpracticed movement directions to targets that were 60/spl deg/ different from the targets used for training. These results demonstrate the potential for these methods for teaching motor skills and for neuro-rehabilitation of brain-injured patients. This is a form of "implicit learning," because unlike explicit training methods, subjects learn movements with minimal instructions, no knowledge of, and little attention to the trajectory.

[1]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[2]  Michael I. Jordan,et al.  Generalization to Local Remappings of the Visuomotor Coordinate Transformation , 1996, The Journal of Neuroscience.

[3]  C. Burgar,et al.  Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis , 1999, IEEE Transactions on Biomedical Engineering.

[4]  W. T. Thach,et al.  Cerebellar ataxia: abnormal control of interaction torques across multiple joints. , 1996, Journal of neurophysiology.

[5]  N. Hogan Mechanical Impedance of Single- and Multi-Articular Systems , 1990 .

[6]  W.Z. Rymer,et al.  of the 23 rd Annual EMBS International Conference , October 25-28 , Istanbul , Turkey ALTERING MOVEMENT PATTERNS IN HEALTHY AND BRAIN-INJURED SUBJECTS VIA CUSTOM DESIGNED ROBOTIC FORCES , 2004 .

[7]  Yoky Matsuoka,et al.  Models of generalization in motor control , 1998 .

[8]  Ferdinando A Mussa-Ivaldi,et al.  Modular features of motor control and learning , 1999, Current Opinion in Neurobiology.

[9]  M. Hallett,et al.  Adaptation to lateral displacement of vision in patients with lesions of the central nervous system , 1983, Neurology.

[10]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  B. Bobath Adult hemiplegia: Evaluation and treatment , 1978 .

[12]  E Knutsson,et al.  Proprioceptive neuromuscular facilitation. , 1980, Scandinavian journal of rehabilitation medicine. Supplement.

[13]  Carol A. Seger,et al.  Implicit learning. , 1994, Psychological bulletin.

[14]  Marsha Johnson-Schmid,et al.  Adult Hemiplegia: Evaluation and Treatment (3rd ed.) , 1991 .

[15]  D J Dewhurst,et al.  Neuromuscular control system. , 1967, IEEE transactions on bio-medical engineering.

[16]  D. Marr A theory of cerebellar cortex , 1969, The Journal of physiology.

[17]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

[18]  C Ghez,et al.  Learning of scaling factors and reference axes for reaching movements. , 1996, Neuroreport.

[19]  J. Patton,et al.  Prespecified After-effects Elicited from Robotic Force Fields , 2000 .

[20]  Scott T. Grafton,et al.  Abstract and Effector-Specific Representations of Motor Sequences Identified with PET , 1998, The Journal of Neuroscience.

[21]  E Bizzi,et al.  Motor learning by field approximation. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[23]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[24]  M. Hallett,et al.  Motor learning in patients with cerebellar dysfunction. , 1990, Brain : a journal of neurology.

[25]  John M. Hollerbach,et al.  Dynamic interactions between limb segments during planar arm movement , 1982, Biological Cybernetics.

[26]  N. Hogan,et al.  Robot‐aided functional imaging: Application to a motor learning study , 1998, Human brain mapping.

[27]  Ferdinando A. Mussa-Ivaldi,et al.  Robots can teach people how to move their arm , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[28]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.

[29]  Jordan Grafman,et al.  Procedural learning is impaired in patients with prefrontal lesions , 1999, Neurology.

[30]  O. Bock Load compensation in human goal-directed arm movements , 1990, Behavioural Brain Research.

[31]  Arthur S. Reber,et al.  Implicit and explicit learning: individual differences and IQ. , 1991 .

[32]  F A Mussa-Ivaldi,et al.  Central representation of time during motor learning. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[33]  J. Albus A Theory of Cerebellar Function , 1971 .

[34]  J. Lackner,et al.  Rapid adaptation to Coriolis force perturbations of arm trajectory. , 1994, Journal of neurophysiology.

[35]  N. Hogan,et al.  Robot-aided neurorehabilitation. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[36]  N. Hogan,et al.  Procedural motor learning in Parkinson's disease , 2001, Experimental Brain Research.

[37]  Paul M. Fitts,et al.  Perceptual-Motor Skill Learning1 , 1964 .

[38]  A. Reber,et al.  Implicit and explicit learning: individual differences and IQ. , 1991, Journal of experimental psychology. Learning, memory, and cognition.

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

[40]  R. Held,et al.  PLASTICITY IN HUMAN SENSORIMOTOR CONTROL. , 1963, Science.

[41]  Barbara A. Leisner,et al.  Proprioceptive Neuromuscular Facilitation (3rd ed.) , 1986 .

[42]  J. Sweatt,et al.  Mechanisms of memory. , 2003, Journal of geriatric psychiatry and neurology.

[43]  Reza Shadmehr,et al.  Computational nature of human adaptive control during learning of reaching movements in force fields , 1999, Biological Cybernetics.

[44]  Mitsuo Kawato,et al.  Feedback-Error-Learning Neural Network for Supervised Motor Learning , 1990 .

[45]  R. Magill 1997 C. H. McCloy Research Lecture: Knowledge is more than we can talk about: implicit learning in motor skill acquisition. , 1998, Research quarterly for exercise and sport.

[46]  R A Scheidt,et al.  Persistence of motor adaptation during constrained, multi-joint, arm movements. , 2000, Journal of neurophysiology.