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This work studies the problem of predicting human intent to interact with a robot in a public environment. To facilitate research in this problem domain, we first contribute the People Approaching Robots Database (PAR-D), a new collection of datasets for intent prediction in Human-Robot Interaction. The database includes a subset of the ATC Approach Trajectory dataset [28] with augmented ground truth labels. It also includes two new datasets collected with a robot photographer on two locations of a university campus. Then, we contribute a novel human-annotated baseline for predicting intent. Our results suggest that the robot’s environment and the amount of time that a person is visible impacts human performance in this prediction task. We also provide computational baselines for intent prediction in PAR-D by comparing the performance of several machine learning models, including ones that directly model pedestrian interaction intent and others that predict motion trajectories as an intermediary step. From these models, we find that trajectory prediction seems useful for inferring intent to interact with a robot in a public environment. 

This review highlights recent advances in the synthesis techniques, morphology control, and emerging applications of Janus particles, serving as a roadmap to guide their design and future applications. 

The diffusion of colloids, nanoparticles, and small molecules near the gas–liquid interface presents interesting multiphase transport phenomena and unique opportunities for understanding interactions near the surface and interface. Stratification happens when different species preside over the interfaces in the final dried coating structure. Understanding the principles of stratification can lead to emerging technologies for materials’ fabrication and has the potential to unlock innovative industrial solutions, such as smart coatings and drug formulations for controlled release. However, stratification can be perplexing and unpredictable. It may involve a complicated interplay between particles and interfaces. The surface chemistry and solution conditions are critical in determining the race of particles near the interface. Current theory and simulation cannot fully explain the observations in some experiments, especially the newly developed stratification of nano-surfactants. Here, we summarize the efforts in the experimental work, theory, and simulation of stratification, with an emphasis on bridging the knowledge gap between our understanding of surface adsorption and bulk diffusion. We will also propose new mechanisms of stratification based on recent observations of nano-surfactant stratification. More importantly, the discussions here will lay the groundwork for future studies beyond stratification and nano-surfactants. The results will lead to the fundamental understanding of nanoparticle interactions and transport near interfaces, which can profoundly impact many other research fields, including nanocomposites, self-assembly, colloidal stability, and nanomedicine.