Fabricating robots from soft materials imposes major constraints on the integration and compatibility of embedded sensing, transmission, and actuation systems. Various soft materials present different challenges, but also new opportunities, for novel fabrication techniques, integrated soft sensors, and embedded actuators. For instance, extensive research on silicone elastomers has led to the development of soft sensors based on closed channels filled with liquid metal conductors, as well as corresponding fluidic actuators by pressurizing cavities within the body. In this paper, we present a novel approach to soft robot fabrication using soft expanding foam as the base material. While recent research points to elastic foams as a means to reduce material, manufacturing costs, and robot mass, they have not been explored much in the literature. This paper presents fabrication and prototyping techniques for developing low cost, custom-shaped soft robots from expanding polyurethane foam. We describe how to integrate user-defined routing points for transmission and actuation through cable-driven electrical actuation systems directly into the foam. Furthermore, we explore novel fabrication and prototyping techniques in order to build and integrate soft sensors into the foam substrate, which we demonstrate on soft robots varying in design complexity from a soft gripper to a soft "puppy".

Cells continuously sense and react to mechanical cues from their surrounding matrix consisting of a fibrous network of biopolymers that influence their fate and behavior. Several powerful methods employing magnetic...

A robust automated system to collect protein crystals for X-ray crystallography is presented. This system uses an ultra violet imaging system based on commercial off the shelf components, a magnetically manipulated tool, and a resilient behavior-based controller. The system is validated by collecting over 350 polystyrene beads, used as crystal emulators, and transporting them 2mm to a predefined goal in a 14 hour period without human intervention. The average time to identify, collect, transport, and deliver a crystal emulator is 2.4 minutes, similar to an expert operator. This is the first demonstration of a completely automated robust system for protein crystal harvesting.