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We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to achieving this is learning perceptual representations that generalize to open-set factors (e.g. novel semantic classes, terrains, dynamic entities) and inferring expert-aligned navigation costs from limited demonstrations. CREStE addresses both these issues, introducing 1) a visual foundation model (VFM) distillation objective for learning open-set structured bird's-eye-view perceptual representations, and 2) counterfactual inverse reinforcement learning (IRL), a novel active learning formulation that uses counterfactual trajectory demonstrations to reason about the most important cues when inferring navigation costs. We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments and find that it outperforms all state-of-the-art approaches with 70% fewer human interventions, including a 2-kilometer mission in an unseen environment with just 1 intervention; showcasing its robustness and effectiveness for long-horizon mapless navigation. Videos and additional materials can be found on the project page: this https URL 

Rules are a critical component of the functioning of nearly every online community, yet it is challenging for community moderators to make data-driven decisions about what rules to set for their communities. The connection between a community's rules and how its membership feels about its governance is not well understood. In this work, we conduct the largest-to-date analysis of rules on Reddit, collecting a set of 67,545 unique rules across 5,225 communities which collectively account for more than 67% of all content on Reddit. More than just a point-in-time study, our work measures how communities change their rules over a 5+ year period. We develop a method to classify these rules using a taxonomy of 17 key attributes extended from previous work. We assess what types of rules are most prevalent, how rules are phrased, and how they vary across communities of different types. Using a dataset of communities' discussions about their governance, we are the first to identify the rules most strongly associated with positive community perceptions of governance: rules addressing who participates, how content is formatted and tagged, and rules about commercial activities. We conduct a longitudinal study to quantify the impact of adding new rules to communities, finding that after a rule is added, community perceptions of governance immediately improve, yet this effect diminishes after six months. Our results have important implications for platforms, moderators, and researchers. We make our classification model and rules datasets public to support future research on this topic. 

Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from being social in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design.