Truss Links are a modular robot platform originally developed for studying Robot Metabolism, the ability for machines to grow, repair, and intelligently adapt by integrating components from their environment or other robots. Here, we investigate whether Truss Links can be more than robots that build themselves: can they serve as a creative medium accessible to non-experts? In this paper, we present a preliminary research-through-design exploration of Truss Links as digital building blocks for the physical world. We demonstrate how Truss Links can be used to animate text, animate two- and three-dimensional shapes, form kinetic sculptures, and hand-build a delta pick-and-place robot. We discuss what this exploration reveals about the design properties that can make a research-grade robotic platform accessible as a creative medium.

Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files of unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model that tracks the 6-DoF transformations of point clusters. Through analyzing cluster movements, we hierarchically address the following challenges: (1) moving part segmentation, (2) body topology inference, and (3) joint parameter estimation. The complete pipeline produces robot description files that are fully compatible with existing simulators. We validate our method across a variety of robots, using both synthetic and real-world scan data. Results indicate that our approach outperforms previous methods in registration and body topology estimation accuracy, offering a scalable solution for automated robot modeling.

Image-text matching is central to visual-semantic cross-modal retrieval and has been attracting extensive attention recently. Previous studies have been devoted to finding the latent correspondence between image regions and words, e.g., connecting key words to specific regions of salient objects. However, existing methods are usually committed to handle concrete objects, rather than abstract ones, e.g., a description of some action, which in fact are also ubiquitous in description texts of real-world. The main challenge in dealing with abstract objects is that there is no explicit connections between them, unlike their concrete counterparts. One therefore has to alternatively find the implicit and intrinsic connections between them. In this paper, we propose a relation-wise dual attention network (RDAN) for image-text matching. Specifically, we maintain an over-complete set that contains pairs of regions and words. Then built upon this set, we encode the local correlations and the global dependencies between regions and words by training a visual-semantic network. Then a dual pathway attention network is presented to infer the visual-semantic alignments and image-text similarity. Extensive experiments validate the efficacy of our method, by achieving the state-of-the-art performance on several public benchmark datasets.