Neural network models of bilateral coordination

Two mechanisms are described for controlling the movement of a pair of arms. The first is an engineered motion planner that finds solutions to the ill-posed problem of making noncolliding, goal-directed movements. The second uses neural networks that learn to emulate the coordinated behaviors of the motion planner using considerably less computational resources. Analysis of the networks shows in general terms how they work, and allows us to make testable predictions about some of the response properties that might be observed in the brain systems serving bilateral coordination.