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   Nguyen, Duc M; Nazeri, Mohammad; Payandeh, Amirreza; Datar, Aniket; Xiao, Xuesu (October 2023, IEEE)

   Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multimodal robot perception data; we collect a large-scale (~100 km, 20 hours, 300 trials, 13 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases.11Website: https://cs.gmu.edu/-xiao/Research/MuSoHu/ 

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3. A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility

   https://doi.org/10.1007/978-3-031-20074-8_18  

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5. Full UHD 360-Degree Video Dataset and Modeling of Rate-Distortion Characteristics and Head Movement Navigation

   https://doi.org/10.1145/3458305.3478447  

   Chakareski, Jacob; Aksu, Ridvan; Swaminathan, Viswanathan; Zink, Michael (June 2021, Proceedings of the 12th ACM Multimedia Systems Conference)

   We investigate the rate-distortion (R-D) characteristics of full ultra-high definition (UHD) 360° videos and capture corresponding head movement navigation data of virtual reality (VR) headsets. We use the navigation data to analyze how users explore the 360° look-around panorama for such content and formulate related statistical models. The developed R-D characteristics and modeling capture the spatiotemporal encoding efficiency of the content at multiple scales and can be exploited to enable higher operational efficiency in key use cases. The high quality expectations for next generation immersive media necessitate the understanding of these intrinsic navigation and content characteristics of full UHD 360° videos. 

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