Customer-obsessed science

Home robots intend to make their users lives easier. Our work moves toward more helpful home robots by enabling them to inform their users of dangerous or unsanitary anomalies in the home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to children. To enable home robots with these abilities, we have created a new dataset

Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to-image matching suffers from false positives caused by matching to local visual patterns. To alleviate this issue, we propose to leverage recent

Information Retrieval (IR) practitioners often train separate ranking models for different domains (geo-graphic regions, languages, stores, websites,...) as it is believed that exclusively training on in-domain data yields the best performance when sufficient data is available. Despite their performance gains, training multiple models comes at a higher cost to train, maintain and update compared to having

Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies, also known as hallucinations, with respect to the provided knowledge, are becoming increasingly important for LLM applications. Current metrics fall short in their ability to provide explainable decisions, systematically check all pieces of information in the response, and are often too computationally expensive to be used

We consider a local planner that utilizes model predictive control to locally deviate from a prescribed global path in response to dynamic environments, taking into account the system dynamics. To ensure the consistency between the local and global paths, we introduce the concept of locally homotopic paths for paths with different origins and destinations. We then formulate a hard constraint to ensure that