Bubbles are lightweight, can be instantly created from minimal material, and their size can be controlled. In this paper, we propose BubbleFab, a fabrication method that enables rapid volumetric prototyping by dynamically generating and stacking plastic bubbles of varying sizes in real time. Using a custom design software, users can generate a stacking script from any 3D model, which is then executed by a robotic arm that autonomously extrudes and assembles the bubbles into a physical structure. The resulting objects are not only lightweight, but can also be reinforced after fabrication for use as molds. Furthermore, the materials used can be recovered and reused. We developed an end-to-end pipeline consisting of dedicated design software and fabrication hardware and evaluated the accuracy and durability of the generated bubbles. We also present a series of example outputs and application scenarios enabled by this method.

We propose Inkjet 4D Print, a self-folding fabrication method of 3D origami tessellations by printing 2D patterns on both sides of a heat-shrinkable base sheet, using a commercialized inkjet ultraviolet (UV) printer. Compared to the previous folding-based 4D printing approach using fused deposition modeling (FDM) 3D printers [An et al. 2018], our method has merits in (1) more than 1200 times higher resolution in terms of the number of self-foldable facets, (2) 2.8 times faster printing speed, and (3) optional full-color decoration. This paper describes the material selection, the folding mechanism, the heating condition, and the printing patterns to self-fold both known and freeform tessellations. We also evaluated the self-folding resolution, the printing and transformation speed, and the shape accuracy of our method. Finally, we demonstrated applications enabled by our self-foldable tessellated objects.

Despite the recent growth in popularity of personal mobility devices (e.g., e-scooters and e-skateboards), they still suffer from limited safety and narrow design form factors, due to their rigid structures. On the other hand, inflatable interfaces studied in human-computer interaction can achieve large volume change by simple inflation/deflation. Inflatable structure also offers soft and safe interaction owing to material compliance and diverse fabrication methods that lead to a wide range of forms and aesthetics. In this paper, we propose poimo, a new family of POrtable and Inflatable MObility devices, which consists of inflatable frames, inflatable wheels, and inflatable steering mechanisms made of a mass-manufacturable material called drop-stitch fabric. First, we defined the basic material properties of a drop-stitch inflatable structure that is sufficiently strong to carry a person while simultaneously allowing soft deformation and deflation for storage and portability. We then implemented an interactive design system that can scan the user's desired riding posture to generate a customized personal mobility device and can add the user's shape and color preferences. To demonstrate the custom-design capability and mobility, we designed several 3D models using our system and built physical samples for two basic templates: a motorcycle and a wheelchair. Finally, we conducted an online user study to examine the usability of the design system and share lessons learned for further improvements in the design and fabrication of poimo.

For the design of artificial systems with the ability to co-create with humans, it is necessary to consider how robots interpret and apply its human partner's actions in its own actions. Nowadays many robots with the ability to co-create with human appeared but few research succeeded in quantifying the interaction between humans and robots, making it difficult to assess the importance of the robot's contribution to the interaction.

There are many robots which have communication skills with human but most of their actions and responses are preprogrammed and few researches inspect psychological aspects of behavior in human-robot co-creation. In this paper, we focus on action-similarity awareness as a key for building up co-creative relationship with humans. Here, action-similarity awareness means that one recognizes affinity to one's own actions derived from other's actions. As a simple task involving action-similarity awareness, we focused on rhythm ensemble. The ensemble is a type of drumming session in which even people who have little musical experience easily take part. In this research we expect to realize the awareness in the rhythm ensemble session by employing the other's past action and mixing it to metrical structure, a basic beat structure in music. To make sure of this idea in real session, we developed a robot system which can actually play a drumming session with humans and control action-similarity awareness. We conducted a subjective experiment and analyzed questionnaire and interaction record. From the questionnaire results, it was confirmed that the sense of prediction increases with action contributing rate and that it is effective to imitate humans actions and add 10% to 30% originality. From the interaction record it was confirmed that phrase structure was shared by making consent on which part to be varied or fixed.