Abstract
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy and to survive in uncertain environments. In this work, a scenario is presented where a robot has limited energy, and the only way to survive is to access the energy from an unregulated power source. With no wires or resistors available, the robot heuristically learns to maximize the input voltage on its system while avoiding potential obstacles during the connection. CircuitBot is a 6 DOF manipulator capable of drawing circuit patterns with graphene-based conductive ink, and it uses a state-of-the-art continuous/categorical Bayesian Optimization to optimize the placement of conductive shapes and maximize the energy it receives. Our comparative results with traditional Bayesian Optimization and Genetic algorithms show that the robot learns to maximize the voltage within the smallest number of trials, even when we introduce obstacles to ground the circuit and steal energy from the robot. As autonomous robots become more present, in our houses and other planets, our proposed method brings a novel way for machines to keep themselves functional by optimizing their own electric circuits.