We present a hierarchical framework for approximately optimal control of redundant manipulators. The plant is augmented with a low-level feedback controller, designed to yield input-output behavior that captures the task-relevant aspects of plant dynamics but has reduced dimensionality. This makes it possible to reformulate the optimal control problem in terms of the augmented dynamics, and optimize a high-level feedback controller without running into the curse of dimensionality. The resulting control hierarchy compares favorably to existing methods in robotics. Furthermore, we demonstrate a number of similarities to �nonhierarchical � optimal feedback control. Besides its engineering applications, the new framework addresses a key unresolved problem in the neural control of movement. It has long been hypothesized that coordination involves selective control of task parameters via muscle synergies, but the link between these parameters and the synergies capable of controlling them has remained elusive. Our framework provides this missing link. © 2005 Wiley Periodicals, Inc. *To whom all correspondence should be addressed.

Abstract – An integrated neuromusculoskeletal (NMS) modeling tool has been developed to facilitate the study of the control of movement in humans and animals. Blocks representing the skeletal linkage, sensors, muscles, and neural controllers are developed using separate software tools and integrated in the powerful simulation environment of Simulink (Mathworks Inc., USA). Musculoskeletal Modeling in Simulink (MMS) converts anatomically accurate musculoskeletal models generated by SIMM (Musculographics Inc., USA) into Simulink blocks. It also removes runtime constraints in SIMM, and allows the development of complex musculoskeletal models without writing a line of code. Virtual Muscle builds realistic Simulink models of muscle force production under physiologic and pathologic conditions. A generic muscle spindle model has also been developed to simulate the sensory output of the primary and secondary afferents. Neural control models developed by various Matlab (Mathworks Inc., USA) toolboxes can be integrated easily with these model components to build complete NMS models in an integrated environment.

In this work we present the first constrained stochastic optimal feedback controller applied to a fully nonlinear, tendon driven index finger model. Our model also takes into account an extensor mechanism, and muscle force-length and force-velocity properties. We show this feedback controller is robust to noise and perturbations to the dynamics, while successfully handling the nonlinearities and high dimensionality of the system. By extending prior methods, we are able to approximate physiological realism by ensuring positivity of neural commands and tendon tensions at all times.

Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research institutions and addresses how increasingly more human-like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.

Abstract — We provide an overview of optimal control methods to nonlinear neuromuscular systems and discuss their limitations. Moreover we extend current optimal control methods to their application to neuromuscular models with realistically numerous musculotendons; as most prior work is limited to torque-driven systems. Recent work on computational motor control has explored the used of control theory and estimation as a conceptual tool to understand the underlying computational principles of neuromuscular systems. After all, successful biological systems regularly meet conditions for stability, robustness and performance for multiple classes of complex tasks. Among a variety of proposed control theory frameworks to explain this, stochastic optimal control has become a dominant framework to the point of being a standard computational technique to reproduce kinematic trajectories of reaching movements (see [12]) In particular, we demonstrate the application of optimal control to a neuromuscular model of the index finger with all seven musculotendons producing a tapping task. Our simulations include 1) a muscle model that includes force- length and force-velocity characteristics; 2) an anatomically plausible biomechanical model of the index finger that includes a tendinous network for the extensor mechanism and 3) a contact model that is based on a nonlinear spring-damper attached at the end effector of the index finger. We demonstrate that it is feasible to apply optimal control to systems with realistically large state vectors and conclude that, while optimal control is an adequate formalism to create computational models of neuromusculoskeletal systems, there remain important challenges and limitations that need to be considered and overcome such as contact transitions, curse of dimensionality, and constraints on states and controls. I.

generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

Abstract — Our long-term goal is to find control principles to control robotic hands with dexterity and robustness comparable to that of the human hand. Here we explore a control strategy capable of accommodating the nonlinearities, high dimensionality and endogenous noise intrinsic to complex biomechanical structures. We present the first stochastic optimal feedback controller applied to a tendon-driven simulated robotic index finger. In our model we take into account the tendon network driving of the index finger, and we consider muscle with the typical force-length force-length and force-velocity properties. Our feedback controller show robustness against noise and perturbation of the dynamics while it can also successfully handle the nonlinearities and high dimensionality of the robotic index finger. In addition, our simulations provide the complete time history of the tendon lengths and the tendon velocities of the index finger for the tasks of tapping with zero and nonzero terminal velocities. I.