Abstract: Across various species and different scales, certain organisms use their appendages to grasp objects not through clamping but through wrapping. This pattern of movement is found in octopus tentacles, elephant trunks, and chameleon prehensile tails, demonstrating a great versatility to grasp a wide range of objects of various sizes and weights as well as dynamically manipulate them in the 3D space. We observed that the structures of these appendages follow a common pattern - a logarithmic spiral - which is especially challenging for existing robot designs to reproduce. This paper reports the design, fabrication, and operation of a class of cable-driven soft robots that morphologically replicate spiral-shaped wrapping. This amounts to substantially curling in length while actively controlling the curling direction as enabled by two principles: a) the parametric design based on the logarithmic spiral makes it possible to tightly pack to grasp objects that vary in size by more than two orders of magnitude and up to 260 times self-weight and b) asymmetric cable forces allow the swift control of the curling direction for conducting object manipulation. We demonstrate the ability to dynamically operate objects at a sub-second level by exploiting passive compliance. We believe that our study constitutes a step towards engineered systems that wrap to grasp and manipulate, and further sheds some insights into understanding the efficacy of biological spiral-shaped appendages. One-Sentence Summary: Design, fabrication, and operation of spiral soft robots at variable scales that can manipulate objects through wrapping. Main Text: INTRODUCTION Wrapping as a paradigm for grasping and manipulation (1), which are two key objectives in robotics (2, 3), is found in the prehensile tail of chameleons and seahorses with length scales as small as a few millimeters (4), as well as in the tentacles of octopuses and the trunks of elephants as large as a meter (Figure 1A) (5, 6).

Picking stuff up seems easy, right? It is – for humans with powerful brain computers that instantly and intuitively figure out everything needed to get the job done. But for robots, even advanced robots, the compute required is surprisingly complex, especially if you want the robot to not, you know, break the thing it's grabbing. MIT has developed a new way to speed up the planning involved in a robot grasping an object, making it "significantly" faster – reducing the total time from as much as ten or more minutes, to under a second. This could have big practical benefits to setting where robotics are already in use, including in industrial environments.