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Inferring limb coordination strategies from trajectory kinematics
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Inferring Limb Coordination Strategies from Trajectory Kinematics John M. Hollerbach MIT Department of Brain and Cognitive Sciences, and Center for Biological Information Processing 545 Technology Square, Cambridge, Mass. 02139, USA Keywords:
motor control-arm movement-kinematics
This work examines the research approach in motor control of deducing planning variables from external observations of movement kinematics. This approach rests on the premise that the motor control system is not capable of executing all possible movements, but possesses restrictions that are manifested as regularities or invariances in arm trajectories. Hence by searching for such invariances, one can attempt to reason backwards to infer the motor control system's structure. A characterization of a trajectory feature as a regularity or invariance is a judgment based on Occam's razor. A set of planning variables is combined with a coordination strategy to predict consequent trajectories. Features of an experimental trajectory are extracted and quantized, and are matched for the best fit to the predicted trajectories. Beginning with the assumption that there is an explicit kinematic plan for all points on a trajectory, one sees how far this assumption can go to explain the experimental data. Two possible sets of kinematic planning variables considered are Cartesian endpoint variables and joint angle variables. The coordination strategy chosen for either planning set is linear interpolation. Predicted trajectories for the two sets of planning variables under linear interpolation are ordinarily distinct: straight lines for Cartesian endpoint variables and curved lines for joint variables. At the same time, there are some pitfalls in this approach. Linear interpolation in joint variables and endpoint variables are merely two simple extremes in a continuum of coordination possibilities. An intermediate strategy of staggered joint interpolation exists that can generate nearly straight lines in certain portions of the workspace. Thus the distinction between joint planning and endpoint planning is not clear cut. There is also a peculiar property of two-link planar kinematics near the workspace boundary, namely the approach to a constant Joint rate ratio. Thus movements studied near the workspace boundary will always appear to have been executed by joint interpolation. Acknowledgements Christopher G. Atkeson has contributed in a major way to this work, which is supported by NIH Grant AM 26710.
Inferring Limb Coordination Strategies from Trajectory Kinematics John M. Hollerbach MIT Department of Brain and Cognitive Sciences, and Center for Biological Information Processing 545 Technology Square, Cambridge, Mass. 02139, USA Keywords:
motor control-arm movement-kinematics
This work examines the research approach in motor control of deducing planning variables from external observations of movement kinematics. This approach rests on the premise that the motor control system is not capable of executing all possible movements, but possesses restrictions that are manifested as regularities or invariances in arm trajectories. Hence by searching for such invariances, one can attempt to reason backwards to infer the motor control system's structure. A characterization of a trajectory feature as a regularity or invariance is a judgment based on Occam's razor. A set of planning variables is combined with a coordination strategy to predict consequent trajectories. Features of an experimental trajectory are extracted and quantized, and are matched for the best fit to the predicted trajectories. Beginning with the assumption that there is an explicit kinematic plan for all points on a trajectory, one sees how far this assumption can go to explain the experimental data. Two possible sets of kinematic planning variables considered are Cartesian endpoint variables and joint angle variables. The coordination strategy chosen for either planning set is linear interpolation. Predicted trajectories for the two sets of planning variables under linear interpolation are ordinarily distinct: straight lines for Cartesian endpoint variables and curved lines for joint variables. At the same time, there are some pitfalls in this approach. Linear interpolation in joint variables and endpoint variables are merely two simple extremes in a continuum of coordination possibilities. An intermediate strategy of staggered joint interpolation exists that can generate nearly straight lines in certain portions of the workspace. Thus the distinction between joint planning and endpoint planning is not clear cut. There is also a peculiar property of two-link planar kinematics near the workspace boundary, namely the approach to a constant Joint rate ratio. Thus movements studied near the workspace boundary will always appear to have been executed by joint interpolation. Acknowledgements Christopher G. Atkeson has contributed in a major way to this work, which is supported by NIH Grant AM 26710.