A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3-D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects’ arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and, from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while after learning, hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that, besides learning a model of the force field, the nervous system also chose to exploit the null space to minimize the effects of the force field on the realization of the endpoint trajectory plan. We discuss applications of these results in the light of current theories of robotic control, including inverse kinematics and optimal control.

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