This thesis introduces a new area of reconfigurable and soft robotics known as Malleable Robots, composed of reconfigurable serial robot arms of low degrees of freedom (DOF) that can modify their topology of revolute joints to generate various robot configurations with alternative workspaces. For the majority of tasks performed by traditional serial robot arms, such as pick and place objectives, only two or three degrees of freedom are required for motion; however, by augmenting the number of degrees of freedom, further dexterity of robot arms for multiple tasks can be achieved. This is demonstrated by the vast majority of serial robot manipulators being composed of 5 or 6 DOF, despite performing low DOF tasks. Through the implementation of reconfigurability to achieve flexibility and adaptation to tasks by morphology changes rather than by increasing the number of joints, malleable robots present advantages over traditional serial robot arms in regards to reduced weight, size, and cost. While limited in degrees of freedom, malleable robots still provide versatility across operations typically served by systems using higher DOF than required by the tasks. This thesis aims to address the design and implementation of the first 2-DOF malleable robot, highlighting their necessary developments, strengths and weaknesses, and suggested areas for implementation.

Firstly, the design of a malleable link is explored; a variable stiffness continuously bending link that enables the key reconfigurability aspect of a malleable robot. The design of a variable stiffness 1 DOF joint is also explored. As this is a new area of robotics, in the second chapter we additionally explore and specify key metrics that are desired for a malleable link, detailing their assessment procedures alongside alternative implementations of malleable links. In the third chapter these are implemented in the creation of a 2-DOF malleable robot, detailed along with its control through forward and inverse kinematics, and a reconfiguration methodology that informs morphology changes based on end effector location---determining how the user should reshape the robot to enable a task previously unattainable. Finally, as a solution to the non-trivial manual reconfiguration of malleable robots, we present the design of an interactive augmented reality alignment interface, which helps a malleable robot understand the users task requirements, visualises to the user the requested robot configuration and its workspace, and guides the user in reconfiguring the robot to achieve that configuration.