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Browsing by Author "Chen, Anqi"

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    Communicating Inferred Goals With Passive Augmented Reality and Active Haptic Feedback
    Mullen, James F.; Mosier, Josh; Chakrabarti, Sounak; Chen, Anqi; White, Tyler; Losey, Dylan P. (IEEE, 2021-10-01)
    Robots learn as they interact with humans. Consider a human teleoperating an assistive robot arm: as the human guides and corrects the arm's motion, the robot gathers information about the human's desired task. But how does the human know what their robot has inferred? Today's approaches often focus on conveying intent: for instance, using legible motions or gestures to indicate what the robot is planning. However, closing the loop on robot inference requires more than just revealing the robot's current policy: the robot should also display the alternatives it thinks are likely, and prompt the human teacher when additional guidance is necessary. In this letter we propose a multimodal approach for communicating robot inference that combines both passive and active feedback. Specifically, we leverage information-rich augmented reality to passively visualize what the robot has inferred, and attention-grabbing haptic wristbands to actively prompt and direct the human's teaching. We apply our system to shared autonomy tasks where the robot must infer the human's goal in real-time. Within this context, we integrate passive and active modalities into a single algorithmic framework that determines when and which type of feedback to provide. Combining both passive and active feedback experimentally outperforms single modality baselines; during an in-person user study, we demonstrate that our integrated approach increases how efficiently humans teach the robot while simultaneously decreasing the amount of time humans spend interacting with the robot. Videos here: https://youtu.be/swq_u4iIP-g
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    An Engineered Reporter Phage for the Fluorometric Detection of Escherichia coli in Ground Beef
    Chen, Anqi; Wang, Danhui; Nugen, Sam R.; Chen, Juhong (MDPI, 2021-02-19)
    Despite enhanced sanitation implementations, foodborne bacterial pathogens still remain a major threat to public health and generate high costs for the food industry. Reporter bacteriophage (phage) systems have been regarded as a powerful technology for diagnostic assays for their extraordinary specificity to target cells and cost-effectiveness. Our study introduced an enzyme-based fluorescent assay for detecting the presence of E. coli using the T7 phage engineered with the lacZ operon which encodes beta-galactosidase (β-gal). Both endogenous and overexpressed β-gal expression was monitored using a fluorescent-based method with 4-methylumbelliferyl β-d-galactopyranoside (MUG) as the substrate. The infection of E. coli with engineered phages resulted in a detection limit of 10 CFU/mL in ground beef juice after 7 h of incubation. In this study, we demonstrated that the overexpression of β-gal coupled with a fluorogenic substrate can provide a straightforward and sensitive approach to detect the potential biological contamination in food samples. The results also suggested that this system can be applied to detect E. coli strains isolated from environmental samples, indicating a broader range of bacterial detection.