arXiv subjects

Fancheng Gao

Publications and source records attributed to Fancheng Gao.

2 recordsLinked to original sources

arXiv2026

Make it Walk: Computational Design of LEGO Walkers

LEGO walkers are walking automata constructed from modular LEGO Technic bricks. They exhibit intriguing locomotion and are also highly reconfigurable through the modular Technic bricks, making them widely applicable in education, recreation, and robotics research. However, existing LEGO walkers are designed mostly by reusing existing single-actuator walking mechanisms. Although the locomotion is stable, their fixed walking gaits limit their adaptability and versatility. This paper presents the first computational solution capable of designing LEGO walkers that not only walk stably, but also support a variety of walking gaits. Our idea is twofold. First, we control the foot motion trajectory of each leg mechanically via a specifically-designed gear-linkage mechanism, in which we incorporate a pair of 3D-printed non-circular gears to work with a LEGO Technic mechanism. Second, we drive each leg by an independent motor and synchronize the timings of multiple leg motions via a simple yet adaptable control algorithm, enabling stable and versatile locomotion. Importantly, we take this modeling as a foundation to co-design the morphology and control of the LEGO walkers to meet the target locomotion and appearance. Leveraging our approach, we created a variety of LEGO walkers, demonstrating various walking gaits and appearances, and fabricated some of them to show their locomotion capability in the real world.

cs.RO↗

arXiv2026

Functionalizing AI-Generated 3D Shapes via Interior Geometry Construction

AI-based 3D generation models such as Hunyuan 3D and Meshy can produce visually appealing 3D shapes from an image or text prompt.However, they typically focus on an object's external appearance, often producing incomplete or implausible interior geometry. Consequently, the generated shapes are not guaranteed to be functionally valid, limiting their direct use in downstream applications such as simulation and fabrication. In this paper, we present a computational approach to functionalize AI-generated 3D shapes through interior geometry construction. Our key observations are that a part's functional role defines its interior geometry, and such geometry is often smooth, relatively simple, and closely related to the part's external shape. Inspired by these observations, we introduce an interior-aware part graph to encode information necessary for constructing interior geometry within an AI-generated 3D shape, and build the graph via VLM-based shape segmentation and graph attribute inference. Guided by the graph, we model the interior geometry of each part as a generalized cylinder and infer its parameters from the part's external geometry through an optimization. The resulting 3D shapes are functionally valid while preserving their AI-generated external appearance. We demonstrate our approach on a variety of AI-generated 3D shapes, including vessels, containers, and storage furniture, and fabricate selected examples for functional validation.

cs.CV↗