Robotic Deconstruction of Brickwork Enabled by Spatial Artificial Intelligence

Robotic deconstruction offers a precise and environmentally responsible alternative to conventional demolition, enabling the selective recovery of building materials for reuse. This research presents a methodology for robotic deconstruction enabled by Spatial Artificial Intelligence (Spatial AI), demonstrated through the case of brickwork. The approach comprises: (1) a deep learning-based real-time object perception system, trained on synthetic photorealistic data to detect and localise individual bricks; (2) the incremental registration and spatial mapping of these discrete elements within an evolving as-built digital twin; and (3) reasoning and control routines that enable perception- and mapping-informed, stepwise robotic deconstruction. The methodology was validated in two progressively complex case studies involving the deconstruction of dry-stacked and mortar-bound brickwork structures with unknown geometries. A mobile robot equipped with an RGB-D camera, gripper, and drill enabled the perception and recovery of individual bricks. Both structures were successfully deconstructed, with variation in manipulation robustness. Results demonstrate the system’s efficacy and broader applicability beyond brickwork.