Soft lightweight robots provide an inherently safe solution to using robots in unmodeled environments by maintaining safety without increasing cost through expensive sensors. Unfortunately, many practical problems still need to be addressed before soft robots can become useful in real world tasks. Unlike traditional robots, soft robot geometry is not constant but can change with deflation and reinflation. Small errors in a robot's kinematic model can result in large errors in pose estimation of the end effector. This error, coupled with the inherent compliance of soft robots and the difficulty of soft robot joint angle sensing, makes it very challenging to accurately control the end effector of a soft robot in task space. However, this inherent compliance means that soft robots lend themselves nicely to coordinated multi-arm manipulation tasks, as deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the focus of this thesis. We first developed two tools enabling multi-arm manipulation with soft robots: (1) a hybrid servoing control scheme for task space control of soft robot arms, and (2) a general base placement optimization for the robot arms in a multi-arm manipulation task. Using these tools, we then developed and implemented a simple multi-arm control scheme. The hybrid servoing control scheme combines inverse kinematics, joint angle control, and task space servoing in order to reduce end effector pose error. We implemented this control scheme on two soft robots and demonstrated its effectiveness in task space control. Having developed a task space controller for soft robots, we then approached the problem of multi-arm manipulation. The placement of each arm for a multi-arm task is non-trivial. We developed an evolutionary optimization that finds the optimal arm base location for any number of user-defined arms in a user-defined task or workspace. We demonstrated the utility of this optimization in simulation, and then used it to determine the arm base locations for two arms in two real world coordinated multi-arm manipulation tasks. Finally, we developed a simple multi-arm control scheme for soft robots and demonstrated its effectiveness using one soft robot arm, and one rigid robot with low-impedance torque control. We placed each arm base in the pose determined by the base placement optimization, and then used the hybrid servoing controller in our multi-arm control scheme to manipulate an object through two desired trajectories.