The fast-growing demand for fully autonomous robots in shared spaces calls for the development of trustworthy agents that can safely and seamlessly navigate in crowded environments. Recent models for motion prediction show promise in characterizing social interactions in such environments. Still, adapting them for navigation is challenging as they often suffer from generalization failures. Prompted by this, we propose Social Robot Tree Search (SoRTS), an algorithm for safe robot navigation in social domains. SoRTS aims to augment existing socially aware motion prediction models for long-horizon navigation using Monte Carlo Tree Search. We use social navigation in general aviation as a case study to evaluate our approach and further the research in full-scale aerial autonomy. In doing so, we introduce XPlaneROS, a high-fidelity aerial simulator that enables human-robot interaction. We use XPlaneROS to conduct a first-of-its-kind user study where 26 FAA-certified pilots interact with a human pilot, our algorithm, and its ablation. Our results, supported by statistical evidence, show that SoRTS exhibits a comparable performance to competent human pilots, significantly outperforming its ablation. Finally, we complement these results with a broad set of self-play experiments to showcase our algorithm's performance in scenarios with increasing complexity.

翻译：随着完全自主机器人在共享空间中需求快速增长，开发能够在拥挤环境中安全无缝导航的可靠智能体迫在眉睫。尽管近期运动预测模型在刻画此类环境中的社交交互方面展现出潜力，但其在导航任务中的适配仍面临泛化失败的挑战。为此，我们提出社交机器人树搜索算法（SoRTS），一种面向社交域安全导航的算法。该算法利用蒙特卡洛树搜索增强现有社交感知运动预测模型，实现长时域导航规划。我们以通用航空中的社交导航为案例评估该方法，并推动全尺寸空中自主性研究。在此过程中，我们构建了支持人机交互的高保真空中模拟器XPlaneROS，并首次开展用户研究：26名美国联邦航空管理局认证飞行员分别与人类飞行员、本算法及其消融版本进行交互。统计证据表明，SoRTS展现出与人类飞行员相当的导航性能，且显著优于消融版本。最后，通过一系列自博弈实验，我们验证了算法在复杂度递增场景中的稳定性。