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Abstract The domestication of agriculture is widely recognized as one of the most crucial technological adaptations for the transition of humanity from hunter-and-gatherer groups into early city-states and ultimately, complex civilizations. As humankind sets forth to permanently establish itself on the Moon and use it as a testing ground to colonize other worlds, like Mars, agriculture will again play a pivotal role. In this case, the development of sustainable crop production systems capable of succeeding in these harsh environments becomes vital to the success of our star-faring journey. Over decades, studies varying in species and approaches have been conducted in microgravity, testing the limits of plants and various growth systems, to better understand how Earth-based agriculture could be translated into environmental conditions and therefore evolutionary pressures beyond what life on our planet has known. While we have passed several significant milestones, we are still far from the goal of a sustainable agricultural system beyond our planet Regolith-based agriculture (RBA) should be a component of sustainable agriculture solutions beyond Earth, one which can also provide insight into plant growth in poor soils across our own world. However, RBA studies are in their infancy and, like any other new field, need an established set of parameters to be followed by the RBA community so the generated data can be standardized and validated. Here, we provide an extensive multi-disciplinary review of the state of RBA, outline important knowledge gaps, and propose a set of standardized methods and benchmarks for regolith simulant development and selection as well as plant, microbe, and plant-microbe interaction studies conducted in lunar and Martian regolith. Our goal is to spur dialog within the RBA community on proper regolith simulant selection, experimental design, and reporting. Our methods are divided into complexity tiers, providing a clear path for even the simplest experiments to contribute to the bulk of the knowledge that will shape the future of RBA science and see it mature as an integrated part of sustainable off-world agriculture. 

As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low-cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of using a calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has led us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle. 

This study evaluated how a robot demonstrating a Theory of Mind (ToM) influenced human perception of social intelligence and animacy in a human-robot interaction. Data was gathered through an online survey where participants watched a video depicting a NAO robot either failing or passing the Sally-Anne false-belief task. Participants (N = 60) were randomly assigned to either the Pass or Fail condition. A Perceived Social Intelligence Survey and the Perceived Intelligence and Animacy subsections of the Godspeed Questionnaire were used as measures. The Godspeed was given before viewing the task to measure participant expectations, and again after to test changes in opinion. Our findings show that robots demonstrating ToM significantly increase perceived social intelligence, while robots demonstrating ToM deficiencies are perceived as less socially intelligent.