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Springtails are tiny arthropods that crawl and jump. They jump by temporarily storing elastic energy in resilin elastic cuticular structures and releasing that energy to accelerate a tail, called a furca, propelling them in the air. This paper presents an autonomous, springtail-inspired microrobot that can crawl and jump. The microrobot has a mass of 980mg and stands 13mm tall, and has on-board sensing, computation, and power, enabling autonomy. The microrobot was designed with a super-elastic shape memory alloy (SMA) spring that is manually loaded to store elastic energy. The on-board sensing and computation triggers an actuator at the jump frequency range that unlatches the spring, launching the microrobot into the air at speeds up to 3.171 m/s. At the same time, the microrobot is capable of crawling, when actuated at frequencies lower or higher than the jump frequency range, demonstrating autonomous multi-modal locomotion. This work opens up new pathways toward autonomy in multi-modal microrobots. 

Distributed manipulators - consisting of a set of actuators or robots working cooperatively to achieve a manipulation task - are robust and flexible tools for performing a range of planar manipulation skills. One novel example is the delta array, a distributed manipulator composed of a grid of delta robots, capable of performing dexterous manipulation tasks using strategies incorporating both dynamic and static contact. Hand-designing effective distributed control policies for such a manipulator can be complex and time consuming, given the high-dimensional action space and unfamiliar system dynamics. In this paper, we examine the principles guiding development and control of such a delta array for a planar translation task. We explore policy learning as a robust cooperative control approach, allowing for smooth manipulation of a range of objects, showing improved accuracy and efficiency over baseline human-designed policies.