Neil Gershenfeld

Alexander Htet Kyaw, Miana Smith, Se Hwan Jeon et al.

We present a system that transforms speech into physical objects using 3D generative AI and discrete robotic assembly. By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. While generative AI models can produce a wide range of 3D meshes, AI-generated meshes are not directly suitable for robotic assembly or account for fabrication constraints. To address this, we contribute a workflow that integrates natural language, 3D generative AI, geometric processing, and discrete robotic assembly. The system discretizes the AI-generated geometry and modifies it to meet fabrication constraints such as component count, overhangs, and connectivity to ensure feasible physical assembly. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a robotic arm.

Faraz Faruqi, Amira Abdel-Rahman, Leandra Tejedor et al.

Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model's structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. We evaluate the effectiveness of FEA simulation feedback in the augmented stylization process by comparing three stylization control strategies. We also investigate the time efficiency of our approach by comparing three adaptive scheduling strategies. Finally, we demonstrate MechStyle's user interface that allows users to generate stylized and structurally viable 3D models and provide five example applications.

Allan Costa, Benjamin Jenett, Irina Kostitsyna et al.

Assembly of large scale structural systems in space is understood as critical to serving applications that cannot be deployed from a single launch. Recent literature proposes the use of discrete modular structures for in-space assembly and relatively small scale robotics that are able to modify and traverse the structure. This paper addresses the algorithmic problems in scaling reconfigurable space structures built through robotic construction, where reconfiguration is defined as the problem of transforming an initial structure into a different goal configuration. We analyze different algorithmic paradigms and present corresponding abstractions and graph formulations, examining specialized algorithms that consider discretized space and time steps. We then discuss fundamental design trades for different computational architectures, such as centralized versus distributed, and present two representative algorithms as concrete examples for comparison. We analyze how those algorithms achieve different objective functions and goals, such as minimization of total distance traveled, maximization of fault-tolerance, or minimization of total time spent in assembly. This is meant to offer an impression of algorithmic constraints on scalability of corresponding structural and robotic design. From this study, a set of recommendations is developed on where and when to use each paradigm, as well as implications for physical robotic and structural system design.