Robotic 3D printing applications are rapidly growing in architecture, where they enable the introduction of new materials and bespoke geometries. However, current approaches remain limited to printing on top of a flat build bed. This limits robotic 3D printing’s impact as a sustainable technology: opportunities to customize or enhance existing elements, or to utilize complex material behaviour are missed. This paper addresses the potentials of conformal 3D printing and presents a novel and robust workflow for printing onto unknown and arbitrarily shaped 3D substrates. The workflow combines dual-resolution Robotic Scanning, Neural Network prediction and printing of PETG plastic. This integrated approach offers the advantage of responding directly to unknown geometries through automated performance design customization. This paper firstly contextualizes the work within the current state of the art of conformal printing. We then describe our methodology and the design experiment we have used to test it. We lastly describe the key findings, potentials and limitations of the work, as well as the next steps in this research.

The objective of autonomous robotic additive manufacturing for construction in the architectural scale is currently being investigated in parts both within the research communities of computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL) in robotics. The presented study summarizes the relevant state of the art in both research areas and lays out how their respective accomplishments can be combined to achieve higher degrees of autonomy in robotic construction within the Architecture, Engineering and Construction (AEC) industry. A distributed control and communication infrastructure for agent training and task execution is presented, that leverages the potentials of combining tools, standards and algorithms of both fields. It is geared towards industrial CDRF applications. Using this framework, a robotic agent is trained to autonomously plan and build structures using two model-free DRL algorithms (TD3, SAC) in two case studies: robotic block stacking and sensor-adaptive 3D printing. The first case study serves to demonstrate the general applicability of computational design environments for DRL training and the comparative learning success of the utilized algorithms. Case study two highlights the benefit of our setup in terms of tool path planning, geometric state reconstruction, the incorporation of fabrication constraints and action evaluation as part of the training and execution process through parametric modeling routines. The study benefits from highly efficient geometry compression based on convolutional autoencoders (CAE) and signed distance fields (SDF), real-time physics simulation in CAD, industry-grade hardware control and distinct action complementation through geometric scripting. Most of the developed code is provided open source.

Nature provides a rich source of information for the design of novel materials; yet there remain significant challenges in the design and manufacture of materials that replicate the form, function, and sustainability of biological solutions. Here, we identify key challenges and promising approaches to the development of materials informed by biology. These challenges fall into two main areas; the first relates to harnessing biological information for materials innovation, including key differences between biological and synthetic materials, and the relationship between structure and function. We propose an approach to materials innovation that capitalizes on biodiversity, together with high-throughput characterization of biological material architectures and properties, linked to environmental and ecological context. The second area relates to the design and manufacture of bioinformed materials, including the physical scale of material architectures and manufacturing scale up. We suggest ways to address these challenges and promising prospects for a bioinformed approach to materials innovation.

This article investigates the critical sustainability challenges posed by Queensland's construction sector through developing a comprehensive circular economy implementation roadmap. Adopting a mixed‐method approach combining quantitative scientometric analysis of academic literature with qualitative systematic review of grey literature, the study bridges theoretical knowledge and practical applications. The findings identify key barriers to circular economy adoption in Queensland's construction industry across environmental, economic, social, and policy dimensions, while highlighting opportunities in deconstruction, material reuse, and waste valorization. The article's primary contribution is an evidence‐based policy roadmap addressing regulatory gaps through targeted incentives, mandates, and reforms. The resulting phased implementation roadmap outlines actionable short‐, medium‐, and long‐term strategies for industry transformation. By aligning Queensland's construction practices with international standards through innovative digital tools, market mechanisms, and multi‐stakeholder engagement, this study provides a practical blueprint for policymakers, industry leaders, and researchers to advance circular construction practices and contribute to sustainable built environment outcomes.