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Recent advances in 3D printing and manufacturing of miniaturized robotichardware and computing are paving the way to build inexpensive and disposablerobots. This will have a large impact on several applications includingscientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g.,operation in confined spaces), and entertainment (e.g., nano drones). The needfor inexpensive and task-specific robots clashes with the current practice,where human experts are in charge of designing hardware and software aspects ofthe robotic platform. This makes the robot design process expensive andtime-consuming, and ultimately unsuitable for small-volumes low-costapplications. This paper considers the computational robot co-design problem,which aims to create an automatic algorithm that selects the best roboticmodules (sensing, actuation, computing) in order to maximize the performance ona task, while satisfying given specifications (e.g., maximum cost of theresulting design). We propose a binary optimization formulation of theco-design problem and show that such formulation generalizes previous workbased on strong modeling assumptions. We show that the proposed formulation cansolve relatively large co-design problems in seconds and with minimal humanintervention. We demonstrate the proposed approach in two applications: theco-design of an autonomous drone racing platform and the co-design of amulti-robot system.

Robot Co-design: Beyond the Monotone Case