In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

This paper presents efficient algorithm and architectures for tensor decomposition based on field programmable gate array (FPGA). Based on the parallel HOOI (PHOOI) algorithm, a circuit structure is presented to reduce the complexity and to improve the efficiency of tensor decomposition engine. Furthermore, a novel algorithm and architecture are proposed to reduce the number of iterations and enhance the throughput. The proposed circuit achieves parallel processing with a minimum increment of hardware complexity. Both proposed architectures are designed and implemented on the FPGA platform. Evaluation results show that the efficiency of proposed architectures is enhanced by 40% and 60% respectively.

We introduce a 3D synthesis method for architectural design to allow for the efficient generation of diverse and realistic building designs. In spite of advances in 3D synthesis, current off-the-shelf 3D synthesis techniques are inappropriate for architectural design: they are trained primarily on isolated objects, have limited diversity, blend building facades with background and produce overly complex geometry that is difficult to edit or manipulate, a major issue in an iterative design process. We propose an alternative pipeline that integrates auto-generated coarse models with segment-wise texture inpainting and semantics-based editing, resulting in diverse, style-consistent, and shape-precise designs. We show through qualitative and quantitative experiments that our pipeline generates more diverse, visually appealing architectures with clean geometries without the need for any extensive training. Project page: https://itingtsai.github.io/syn_arch_2025/