1462 Part G Human-Centered and Life-Like Robotics ecuted (see Flanagan and Johansson [62.63],Flanagan tor and parietal neurons suggest a premotor mechanism et al.[62.64],and Mataric and Pomplun [62.651).The of attention that deserves exploration in further work in early responses,before action onset,of many premo- neurorobotics. 62.3 The Role of the Cerebellum Although cerebellar involvement in muscle control was grasp the object.Thus analysis of how various compo- advocated long ago by the Greek gladiator surgeon nents of cerebral cortex interact to support forward and Galen of Pergamum (129-216/17 CE),it was the publi- inverse models which determine the overall shape of cation by Eccle et al.[62.66]of the first comprehensive the behavior must be complemented by analysis of how account of the detailed neurophysiology and anatomy the cerebellum handles control delays and nonlineari- of the cerebellum (/to [62.671)that provided the inspi-ties to transform a well-articulated plan into graceful ration for the Marr-Albus model of cerebellar plasticity coordinated action.Within this perspective,cerebellar (Marr [62.68];Albus [62.69])that is at the heart of structure and function will be very helpful in the control most current modeling of the role of the cerebellum of a new class of highly antagonistic robotic systems as in control of motion and sensing.From a robotics well as in adaptive control. point of view,the most convincing results are based on Albus'[62.70]cerebellar model articulation con- 62.3.1 The Human Control Loop troller(CMAC)model and subsequent implementations by Miller [62.71].These models,however,are only Lesions and deficits of the cerebellum impair the co- remotely based on the structure of the biological cerebel- ordination and timing of movements while introducing lum.More detailed models are usually only applied to excessive.undesired motion:effects which cannot be two-degree-of-freedom robotic structures,and have not compensated by the cerebral cortex.According to main- been generalized to real-world applications(see Peters stream models,the cerebellum filters descending motor and van der Smagt [62.72]).The problem may lie with cortex commands to cope with timing issues and com- viewing the cerebellum as a stand-alone dynamics con- munication delays which go up to 50 ms one way for arm troller.An important observation about the brain is that control.Clearly,closed-loop control with such delays is schemas are widely distributed,and different aspects of not viable in any reasonable setting,unless augmented the schemas are computed in different parts of the brain. with an open-loop component,predicting the behavior of Thus,one view is that (1)the cerebral cortex has the the actuator system.This is where the cerebellum comes necessary models for choosing appropriate actions and into its own.The complexity of the vertebrate muscu- getting the general shape of the trajectory assembled loskeletal system,clearly demonstrated by the human to fit the present context,whereas (2)the cerebellum arm using a total of 19 muscle groups for planar mo- provides a side-path which (on the basis of extensive tion of the elbow and shoulder alone (see Nijhof and learning of a forward motor model)provides the ap- Kouwenhoven [62.73])requires a control mechanism propriate corrections to compensate for control delays,coping with this complexity,especially in a setting with Part muscle nonlinearities,Coriolis and centrifugal forces long control delays.One cause for this complexity is occasioned by joint interactions,and subtle adjustments that animal muscles come in antagonistic pairs (e.g., 9 of motor neuron firing in simultaneously active motor flexing versus extending a joint).Antagonistic control 23 pattern generators to ensure their smooth coordination.of muscle groups leads to energy-optimal(Damsgaard Thus,for example,a patient with cerebellar lesions may et al.[62.741)and intrinsically flexible systems.Contact be able to move his arm to successfully reach a target,with stiff or fast-moving objects requires such flexibil- and to successfully adjust his hand to the size of an ob-ity to prevent breakage.In contrast,classical (industrial) ject.However,he lacks the machinery to perform either robots are stiff,with limb segments controlled by lin- action both swiftly and accurately,and further lacks the ear or rotary motors with gear boxes.Even so,most ability to coordinate the timing of the two subactions. laboratory robotic systems have passively stiff joints, His behavior will thus exhibit decomposition of move- with active joint flexibility obtainable only by using fast ment-he may first move the hand till the thumb touches control loops and joint torque measurement.Although the object,and only then shape the hand appropriately to it may be debatable whether such robotic systems re-1462 Part G Human-Centered and Life-Like Robotics ecuted (see Flanagan and Johansson [62.63], Flanagan et al. [62.64], and Mataric and Pomplun [62.65]). The early responses, before action onset, of many premo￾tor and parietal neurons suggest a premotor mechanism of attention that deserves exploration in further work in neurorobotics. 62.3 The Role of the Cerebellum Although cerebellar involvement in muscle control was advocated long ago by the Greek gladiator surgeon Galen of Pergamum (129–216/17 CE), it was the publi￾cation by Eccle et al. [62.66] of the first comprehensive account of the detailed neurophysiology and anatomy of the cerebellum (Ito [62.67]) that provided the inspi￾ration for the Marr–Albus model of cerebellar plasticity (Marr [62.68]; Albus [62.69]) that is at the heart of most current modeling of the role of the cerebellum in control of motion and sensing. From a robotics point of view, the most convincing results are based on Albus’ [62.70] cerebellar model articulation con￾troller (CMAC) model and subsequent implementations by Miller [62.71]. These models, however, are only remotely based on the structure of the biological cerebel￾lum. More detailed models are usually only applied to two-degree-of-freedom robotic structures, and have not been generalized to real-world applications (see Peters and van der Smagt [62.72]). The problem may lie with viewing the cerebellum as a stand-alone dynamics con￾troller. An important observation about the brain is that schemas are widely distributed, and different aspects of the schemas are computed in different parts of the brain. Thus, one view is that (1) the cerebral cortex has the necessary models for choosing appropriate actions and getting the general shape of the trajectory assembled to fit the present context, whereas (2) the cerebellum provides a side-path which (on the basis of extensive learning of a forward motor model) provides the ap￾propriate corrections to compensate for control delays, muscle nonlinearities, Coriolis and centrifugal forces occasioned by joint interactions, and subtle adjustments of motor neuron firing in simultaneously active motor pattern generators to ensure their smooth coordination. Thus, for example, a patient with cerebellar lesions may be able to move his arm to successfully reach a target, and to successfully adjust his hand to the size of an ob￾ject. However, he lacks the machinery to perform either action both swiftly and accurately, and further lacks the ability to coordinate the timing of the two subactions. His behavior will thus exhibit decomposition of move￾ment – he may first move the hand till the thumb touches the object, and only then shape the hand appropriately to grasp the object. Thus analysis of how various compo￾nents of cerebral cortex interact to support forward and inverse models which determine the overall shape of the behavior must be complemented by analysis of how the cerebellum handles control delays and nonlineari￾ties to transform a well-articulated plan into graceful coordinated action. Within this perspective, cerebellar structure and function will be very helpful in the control of a new class of highly antagonistic robotic systems as well as in adaptive control. 62.3.1 The Human Control Loop Lesions and deficits of the cerebellum impair the co￾ordination and timing of movements while introducing excessive, undesired motion: effects which cannot be compensated by the cerebral cortex. According to main￾stream models, the cerebellum filters descending motor cortex commands to cope with timing issues and com￾munication delays which go up to 50 ms one way for arm control. Clearly, closed-loop control with such delays is not viable in any reasonable setting, unless augmented with an open-loop component, predicting the behavior of the actuator system. This is where the cerebellum comes into its own. The complexity of the vertebrate muscu￾loskeletal system, clearly demonstrated by the human arm using a total of 19 muscle groups for planar mo￾tion of the elbow and shoulder alone (see Nijhof and Kouwenhoven [62.73]) requires a control mechanism coping with this complexity, especially in a setting with long control delays. One cause for this complexity is that animal muscles come in antagonistic pairs (e.g., flexing versus extending a joint). Antagonistic control of muscle groups leads to energy-optimal (Damsgaard et al. [62.74]) and intrinsically flexible systems. Contact with stiff or fast-moving objects requires such flexibil￾ity to prevent breakage. In contrast, classical (industrial) robots are stiff, with limb segments controlled by lin￾ear or rotary motors with gear boxes. Even so, most laboratory robotic systems have passively stiff joints, with active joint flexibility obtainable only by using fast control loops and joint torque measurement. Although it may be debatable whether such robotic systems re￾Part G 62.3