Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots that can deform. In addition, the benefit of being able to cheaply manufacture soft robots may be lost if the tactile sensors that cover them are expensive and their resolution does not scale well for manufacturability. Soft robots not only need to know their interaction forces due to contact with their environment, they also need to know where they are in Cartesian space. Because soft robots lack a rigid structure, traditional methods of joint estimation found in rigid robots cannot be employed on soft robotic platforms. This requires a different approach to soft robot pose estimation. This thesis will discuss both tactile force sensing and pose estimation methods for soft-robots. A method to make affordable, high-resolution, tactile sensor arrays (manufactured in rows and columns) that can be used for sensorizing soft robots and other soft bodies isReserved developed. However, the construction results in a sensor array that exhibits significant amounts of cross-talk when two taxels in the same row are compressed. Using the same fabric-based tactile sensor array construction design, two different methods for cross-talk compensation are presented. The first uses a mathematical model to calculate a change in resistance of each taxel directly. The second method introduces additional simple circuit components that enable us to isolate each taxel electrically and relate voltage to force directly. This thesis also discusses various approaches in soft robot pose estimation along with a method for characterizing sensors using machine learning. Particular emphasis is placed on the effectiveness of parameter-based learning versus parameter-free learning, in order to determine which method of machine learning is more appropriate and accurate for soft robot pose estimation. Various machine learning architectures, such as recursive neural networks and convolutional neural networks, are also tested to demonstrate the most effective architecture to use for characterizing soft-robot sensors.

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Legged robots have the potential to cover terrain not accessible to wheel-based robots and vehicles. This makes them better suited to perform tasks, such as search and rescue, in real-world unstructured environments. Pneumatically-actuated, compliant robots are also more suited than their rigid counterparts to work in real-world unstructured environments with humans where unintentional contact may occur. This thesis seeks to combine the benefits of these two type of robots by implementing design methods to aid in the design choice of a 16 degree of freedom (DoF) compliant, continuum-joint quadruped. This work focuses on the design optimization, especially the definition of design metrics, for this type of robot. The work also includes the construction and closed-loop control of a four-DoF continuum-joint leg used to validate design methods.We define design metrics for legged robot metrics that evaluate their ability to traverse unstructured terrain, carry payloads, find stable footholds, and move in desired directions. These design metrics require a sampling of a legged-robot's complete configuration space. For high-DoF robots, such as the 16-DoF in evaluated in this work, the evaluation of these metrics become intractable with contemporary computing power. Therefore, we present methods that can be used to simplify and approximate these metrics. These approximations have been validated on a simulated four-DoF legged robot where they can tractably be compared against their full counterparts.Using the approximations of the defined metrics, we have performed a multi-objective design optimization to investigate the ten-dimensional design space of a 16-DoF compliant, continuum-joint quadruped. The design variables used include leg link geometry, robot base dimensions, and the leg mount angles. We have used an evolutionary algorithm as our optimization method which converged on a Pareto front of optimal designs. From these set of designs, we are able to identify the trade-offs and design differences between robots that perform well in each of the different design metrics. Because of our approximation of the metrics, we were able to perform this optimization on a supercomputer with 28 cores in less than 40 hours.We have constructed a 1.3 m long continuum-joint leg from one of the resulting quadruped designs of the optimization. We have implemented configuration estimation and control and force control on this leg to evaluate the leg payload capability. Using these controllers, we have conducted an experiment to compare the leg's ability to provide downward force in comparison with its theoretical payload capabilities. We then demonstrated how the torque model used in the calculation of payload capabilities can accurately calculate trends in force output from the leg.

Robot assisted therapy is becoming increasingly popular. Research has proven it can be of benefit to persons dealing with a variety of disorders, such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and it can also provide a source of emotional support e.g. to persons living in seniors’ residences. The advancement in technology and a decrease in cost of products related to consumer electronics, computing and communication has enabled the development of more advanced social robots at a lower cost. This brings us closer to developing such tools at a price that makes them affordable to lower income individuals and families. Currently, in several cases, intensive treatment for patients with certain disorders (to the level of becoming effective) is practically not possible through the public health system due to resource limitations and a large existing backlog. Pursuing treatment through the private sector is expensive and unattainable for those with a lower income, placing them at a disadvantage. Design and effective integration of technology, such as using social robots in treatment, reduces the cost considerably, potentially making it financially accessible to lower income individuals and families in need. The Objective of the research reported in this manuscript is to design and implement a social robot that meets the low-cost criteria, while also containing the required functions to support children with ASD. The design considered contains knowledge acquired in the past through research involving the use of various types of technology for the treatment of mental and/or emotional disabilities.