We develop a novel method printing complex self-folding geometries. We demonstrated that with a desktop fused deposition modeling (FDM) 3D printer, off-the-shelf printing filaments and a design editor, we can print flat thermoplastic composites and trigger them to self-fold into 3D with arbitrary bending angles. This is a suitable technique, called Thermorph, to prototype hollow and foldable 3D shapes without losing key features. We describe a new curved folding origami design algorithm, compiling given arbitrary 3D models to 2D unfolded models in G-Code for FDM printers. To demonstrate the Thermorph platform, we designed and printed complex self-folding geometries (up to 70 faces), including 15 self-curved geometric primitives and 4 self-curved applications, such as chairs, the simplified Stanford Bunny and flowers. Compared to the standard 3D printing, our method saves up to 60% - 87% of the printing time for all shapes chosen.

We present a printed paper actuator as a low cost, reversible and electrical actuation and sensing method. This is a novel but easily accessible enabling technology that expands upon the library of actuation-sensing materials in HCI. By integrating three physical phenomena, including the bilayer bending actuation, the shape memory effect of the thermoplastic and the current-driven joule heating via conductive printing filament, we developed the actuator by simply printing a single layer conductive Polylactide (PLA) on a piece of copy paper via a desktop fused deposition modeling (FDM) 3D printer. This paper describes the fabrication process, the material mechanism, and the transformation primitives, followed by the electronic sensing and control methods. A software tool that assists the design, simulation and printing toolpath generation is introduced. Finally, we explored applications under four contexts: robotics, interactive art, entertainment and home environment.

This paper presents an end-to-end approach to automate the design and fabrication process for self-folding origami structures. Self-folding origami structures are robotic sheets composed of rigid tiles and joint actuators. When they are exposed to heat, each joint folds into a preprogrammed angle. Those folding motions transform themselves into a structure, which can be used as body of 3-D origami robots, including walkers, analog circuits, rotational actuators, and microcell grippers. Given a 3-D model, the design algorithm automatically generates a layout printing design of the sheet form of the structure. The geometric information, such as the fold angles and the folding sequences, is embedded in the sheet design. When the sheet is printed and baked in an oven, the sheet self-folds into the given 3-D model. We discuss, first, the design algorithm generating multiple-step self-folding sheet designs, second, verification of the algorithm running in O(n2) time, where n is the number of the vertices, third, implementation of the algorithm, and finally, experimental results, several self-folded 3-D structures with up to 55 faces and two sequential folding steps.

A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.

This paper presents an end-to-end approach for creating 3D shapes by self-folding planar sheets activated by uniform heating. These shapes can be used as the mechanical bodies of robots. The input to this process is a 3D geometry (e.g. an OBJ file). The output is a physical object with the specified geometry. We describe an algorithm pipeline that (1) identifies the overall geometry of the input, (2) computes a crease pattern that causes the sheet to self-fold into the desired 3D geometry when activated by uniform heating, (3) automatically generates the design of a 2D sheet with the desired pattern and (4) automatically generates the design files required to fabricate the 2D structure. We demonstrate these algorithms by applying them to complex 3D shapes. We demonstrate the fabrication of a self-folding object with over 50 faces from automatically generated design files.

This paper describes a robot in the form of a self-folding sheet that is capable of origami-style autonomous folding. We describe the hardware device we designed and fabricated. The device is a sheet with a box-pleated pattern and an integrated electronic substrate and actuators. The sheet is programmed and controlled to achieve different shapes using an idea called sticker programming. We describe the sticker controller and its instantiation. We also describe the algorithms for programming and controlling a given sheet to self-fold into a desired shape. Finally we present experiments with a 4×4 hardware device and an 8×8 hardware device.

We present a distributed algorithm for creating a modular shape by magnification. The input to the algorithm is presented with a small scale version of the desired shape and a magnification factor m . The output of the system is the object that corresponds to the m-fold magnification of the input shape. We describe and analyze a distributed algorithm for this capability and present simulation results. Making shapes by magnification can be viewed as a programming interface for creating objects by programming matter.