AutoFac: The Perpetual Robot Machine

Geoff Nitschke, David Howard

Abstract: Robotics currently lacks fully autonomous capabilities, especially where task knowledge is incomplete and optimal robotic solutions cannot be pre-engineered. The intersection of <i>evolutionary robotics</i>, <i>artificial life</i>, and <i>embodied artificial intelligence</i> presents a promising paradigm for generating multitask problem-solvers suitable for adapting over extended periods in unexplored, remote, and hazardous environments. To address the automation of evolving robotic systems, we propose fully autonomous embodied artificial-life factories and laboratories, situated in various environments as multitask problem solvers. Such integrated factories and laboratories would be adaptive solution designers, producing fit-for-purpose physical robots with accelerated artificial evolution that experiment to continually discover new tasks. Such tasks would be <i>stepping-stones</i> toward accomplishing given mission objectives over extended periods (days to decades). Rather than being purely speculative, prerequisite technologies to realize such factories have been experimentally demonstrated. Currently, vast scientific and enterprise opportunities await in applications such as asteroid mining, terraforming, space, and deep-sea exploration, though no suitable solution exists. The proposed embodied artificial-life factories and laboratories, termed <i>AutoFac</i>, use robot production equipment run by artificial evolution controllers to collect and synthesize environmental information (from robotic sensory systems). Such information is merged with current needs and mission objectives to create new robot embodiment and task definitions that are environmentally adapted and balance task-oriented behavior with exploration. AutoFac is, thus, generalist (deployable in many environments) but continually produces specialist solutions within such environments&#x2014;a perpetual robot machine.