Towards a Distributed, Robotically Assisted Construction Framework

In this paper we document progress towards an architectural framework for adaptive and distributed robotically assisted construction. Drawing from state-of-the-art reinforcement learning techniques, our framework allows for a variable number of robots to adaptively execute simple construction tasks. The paper describes the framework, demonstrates its potential through simulations of pick-and-place and spray-coating construction tasks conducted by a fleet of drones, and outlines a proof-of-concept experiment. With these elements the paper contributes to current research in architectural and construction robotics, particularly to efforts towards more adaptive and hybrid human-machine construction ecosystems. The code is available at: https://github.com/c0deLab/RAiC