Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and data scarcity. In this work, we perform a methodical investigation of the effects of human motion prediction quality on robot navigation performance, as well as human productivity and impressions. We design a scenario involving robot navigation among two human subjects in a constrained workspace and instantiate it in a user study ($N=80$) involving two different robot platforms, conducted across two sites from different world regions. Key findings include evidence that: 1) the widely adopted average displacement error is not a reliable predictor of robot navigation performance and human impressions; 2) the common assumption of human cooperation breaks down in constrained environments, with users often not reciprocating robot cooperation, and causing performance degradations; 3) more efficient robot navigation often comes at the expense of human efficiency and comfort.

翻译：受将移动机器人更紧密地集成到仓库、医院、制造工厂和家庭这一愿景的驱动，我们聚焦于机器人在动态且空间受限环境中的导航。在此类场景中，确保人类的安全、舒适和效率要求机器人具备人类在其周围移动的模型。由于人类行为的随机性、用户偏好的差异以及数据稀缺性，机器人周围的人类运动预测尤其具有挑战性。在本工作中，我们系统性地研究了人类运动预测质量对机器人导航性能以及人类生产效率和印象的影响。我们设计了一个涉及机器人在受限工作空间中于两名人类受试者之间导航的场景，并通过一项用户研究（$N=80$）将其具体化，该研究涉及两个不同的机器人平台，并在来自世界不同地区的两个地点进行。关键发现包括证据表明：1）被广泛采用的平均位移误差并非机器人导航性能和人类印象的可靠预测指标；2）关于人类合作的常见假设在受限环境中失效，用户常常不回应机器人的合作，从而导致性能下降；3）更高效的机器人导航往往以牺牲人类的效率和舒适度为代价。