Soft materials have enabled the fabrication of novel robots with interesting and complex capabilities. The same properties that have enabled these innovations—continuous deformation, elasticity, and low elastic moduli—are the same properties that make soft robotics challenging. Soft robots have limited load-bearing capabilities, making it difficult to use them when manipulation of heavy objects is needed, for example. The ability for soft robots to deform continuously makes it difficult to model and control them, as well as impart them with adequate proprioception. This dissertation presents work that attempts to address these two main challenges by increasing load-bearing ability and improving sensing. I present a composite material comprising an open-cell foam of silicone rubber infiltrated with a low melting-temperature metal. The composite has two stiffness regimes—a rigid regime at room temperature dominated by the solid metal, and an elastomeric regime at above the melting temperature of the metal, which is dictated by the silicone. I characterize the mechanical properties of the composite material and demonstrate its ability to hold different shapes, self-heal, and actuate using shape memory. In an advance for soft robotic sensing, I present a silicone foam embedded with optical fibers that can detect when it is being bent or twisted. I applied machine learning techniques to the diffuse reflected light exiting the optical fibers to detect deformation as well as predict the magnitude of that deformation. The best models predicted the angle of bend and twist with a mean absolute error of 0.06 degrees. However, the model accuracy decreases with time due to drift of the constitutive optical fiber light intensity values. I lastly present research that reduces model error due to sensor drift using data augmentation.