This paper presents two approaches toward the understanding and realization of constrained motion of the upper limbs. The first approach deals with the analysis on the constrained human movements under the framework of optimal control. It is shown that the combination of the optimality criteria constructed by muscle force change and hand contact force change can explain the formation of the constrained reaching movements. The second approach comes from robotics. It is illustrated by application of reinforcement learning to a robotic manipulation problem. Here, an assumption on the existence of holonomic constraints can accelerate learning by introducing function approximation techniques within model-based reinforcement learning. The idea of estimation (parameterization) of the constraints can relate two different problems under the common perspective of “learning constrained motion.”