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Research Area

Robotics

Delivering a more convenient and consistent customer experience through a variety of technologies, including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.

Recent publications

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  • Alessandro Suglia, Qiaozi (QZ) Gao, Jesse Thomason, Govind Thattai, Gaurav Sukhatme
    EMNLP 2021 Workshop on Novel Ideas in Learning-to-Learn through Interaction
    2021
    Language-guided robots performing home and office tasks must navigate in and interact with the world. Grounding language instructions against visual observations and actions to take in an environment is an open challenge. We present Embodied BERT (EmBERT), a transformer-based model which can attend to high-dimensional, multi-modal inputs across long temporal horizons for language-conditioned task completion
  • Bouchra Bouqata, Krishna Aswani, David Bailey
    ArabWIC 2021
    2021
    The recent rapid progress of deep learning algorithms in generating realistic images, especially in Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE), has helped advance new applications. Examples of such applications range from generating and manipulating new synthetic data for self-driving cars, to building/urban architectures, to interior design, and gaming. Furthermore, several
  • Samer B. Nashed, Jong Jin Park, Roger Webster, Joseph W. Durham
    IROS 2021
    2021
    Autonomous mobile robots need maps for effective, safe navigation, and SLAM in general is still an unsolved problem. Nonetheless, certain combinations of environmental characteristics and sensors admit tractable solutions. In particular, detection and tracking of linear features such as line segments (2D) or planar facets (3D) has been proven robust in many man-made environments. However, these types of
  • IROS 2021
    2021
    Localization is an essential module that supports many intelligent functions of a mobile robot such as transportation or inspection. However, justifying that a localization module is sufficiently accurate for supporting all downstream tasks is one of the most difficult questions to answer in practice. To overcome this problem, we move away from the traditional calculation of pose errors and propose a new
  • Yizhou Zhao, Kaixiang Lin, Zhiwei Jia, Qiaozi (QZ) Gao, Govind Thattai, Jesse Thomason, Gaurav Sukhatme
    NeurIPS 2021 Workshop on CtrlGen
    2021
    Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts. This paper presents LUMINOUS, the first research framework that employs stateof-the-art indoor scene

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