Malleable Robots
Angus B. Clark, Xinran Wang, Alex Ranne, Nicolas Rojas
TL;DR
This work introduces malleable robots—low-DOF manipulators with adjustable stiffness to achieve high dexterity via reconfigurable topologies. It integrates layer-jamming based malleable links, flexible spines, and a distance-geometry framework to compute workspaces and kinematics, enabling topology-aware control with reduced actuator counts. The paper also presents an augmented reality (AR)–assisted reconfiguration workflow and data-efficient learning-based control (neural networks and Gaussian processes) to realize autonomous reconfiguration and operation of soft, compliant robotic systems. Together, these contributions offer a pathway toward flexible, cost-effective manufacturing automation with reconfigurable workspaces, AR-guided human-robot collaboration, and data-efficient control of continuum-like malleable robots.
Abstract
This chapter is about the fundamentals of fabrication, control, and human-robot interaction of a new type of collaborative robotic manipulators, called malleable robots, which are based on adjustable architectures of varying stiffness for achieving high dexterity with lower mobility arms. Collaborative robots, or cobots, commonly integrate six or more degrees of freedom (DOF) in a serial arm in order to allow positioning in constrained spaces and adaptability across tasks. Increasing the dexterity of robotic arms has been indeed traditionally accomplished by increasing the number of degrees of freedom of the system; however, once a robotic task has been established (e.g., a pick-and-place operation), the motion of the end-effector can be normally achieved using less than 6-DOF (i.e., lower mobility). The aim of malleable robots is to close the technological gap that separates current cobots from achieving flexible, accessible manufacturing automation with a reduced number of actuators.
