6 papers
SEGAR: Selective Enhancement for Generative Augmented Reality
Fanjun Bu, Chenyang Yuan, Hiroshi Yasuda
Generative world models offer a compelling foundation for augmented-reality (AR) applications: by predicting future image sequences that incorporate deliberate visual edits, they e…
Using Vision-Language Models as Proxies for Social Intelligence in Human-Robot Interaction
Fanjun Bu, Melina Tsai, Audrey Tjokro +3
Robots operating in everyday environments must often decide when and whether to engage with people, yet such decisions often hinge on subtle nonverbal cues that unfold over time an…
The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets
Matt Franchi, Maria Teresa Parreira, Fanjun Bu +1
This paper introduces the Robotability Score (), a novel metric that quantifies the suitability of urban environments for autonomous robot navigation. Through expert interviews…
Making Sense of Robots in Public Spaces: A Study of Trash Barrel Robots
Fanjun Bu, Kerstin Fischer, Wendy Ju
In this work, we analyze video data and interviews from a public deployment of two trash barrel robots in a large public space to better understand the sensemaking activities peopl…
ReStory: VLM-augmentation of Social Human-Robot Interaction Datasets
Fanjun Bu, Wendy Ju
Internet-scaled datasets are a luxury for human-robot interaction (HRI) researchers, as collecting natural interaction data in the wild is time-consuming and logistically challengi…
Boosting Visual Fidelity in Driving Simulations through Diffusion Models
Fanjun Bu, Hiroshi Yasuda
Diffusion models have made substantial progress in facilitating image generation and editing. As the technology matures, we see its potential in the context of driving simulations…