ABSTRACT
From a robotics perspective, the human body is a complex, high degree-of-freedom motor system, with a plethora of sensory inputs, motor outputs, and multiple endeffectors. The challenge for the nervous system is to synergistically coordinate motion
and forces from collective muscles, limbs, and joints, to skillfully and efficiently achieve tasks such as reaching, grasping, and walking. The complexity of planning and controlling such skills is daunting, particularly as most of the motions we execute are redundant (having an infinite set of possible motor inputs that can achieve the same motor output; Figure 6.1). How the nervous system chooses its particular motor solution, even for the simplest of tasks, is still an active topic in neuroscience research. Roboticists, however, commonly work with redundant systems, and have developed computational methods for reducing complexity. Given a model of a redundant robotic system, with specified end-effector(s), techniques exist for decomposing the robot’s kinematics and dynamics into the task space (the lower-dimensional subspace of motion and forces directly relevant for task achievement). Orthogonal to the task space is the null space (the subspace of task-irrelevant motion and forces). These decomposition methods enable the planning and control of tasks in a simplified, reduced dimensional space, while still allowing for exploitation of redundancy for achieving secondary tasks or absorbing disturbances.
